February 2014 

NASA/CR–2014-218167 

Airborne Forward-Looking Interferometer 

for the Detection of Terminal-Area Hazards 

Leanne West, Gary Gimmestad, and Sarah Lane 

Georgia Tech Research Institute, Atlanta, Georgia 

Bill L. Smith, Stanislav Kireev, and Taumi S. Daniels 

Hampton University, Hampton, Virginia 

Larry Cornman and Bob Sharman 

STAR Institute, Boulder, Colorado 
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February 2014 

NASA/CR–2014-218167 

Airborne Forward-Looking Interferometer 

for the Detection of Terminal-Area Hazards 

Leanne West, Gary Gimmestad, and Sarah Lane 

Georgia Tech Research Institute, Atlanta, Georgia 

Bill L. Smith, Stanislav Kireev, and Taumi S. Daniels 

Hampton University, Hampton, Virginia 

Larry Cornman and Bob Sharman 

STAR Institute, Boulder, Colorado 
Available from: 

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7115 Standard Drive 

Hanover, MD 21076-1320 

443-757-5802 

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1 

Executive Summary 

Prior to the three-year project summarized here, radiometric efforts to detect aviation hazards 

were largely experimental, and they were limited to single-line-of sight broadband instruments. In 

general, the experiments were not adequately supported by simulation and modeling, and truth 

data were usually not recorded in the field, so it was not possible to know what the radiometers 

were looking at. Despite tantalizing results dating back to the late 1970s, radiometric detection of 

aviation hazards did not live up to its promise, with the exception of volcanic ash detection 

research, which resulted in commercial instruments. In the Forward Looking Interferometer (FLI) 

program, we investigated the use of imaging radiometers with high spectral resolution, using both 

modeling/simulation and field experiments, along with sophisticated data analysis techniques that 

were originally developed for analysis of data from space-based radiometers and hyperspectral 

imagers. By these means, we have greatly advanced the state of knowledge in this technical area, 

and the FLI program developed a greatly improved understanding of the radiometric signal 

strength of aviation hazards in a wide range of scenarios, in addition to a much better 

understanding of the real-world functionality requirements for hazard detection instruments. 

During the course of the project, we conducted field experiments on three hazards (turbulence, 

runway conditions, and wake vortices) and analytical studies on several others. The main findings 

of the project are summarized in the sections below. 

Turbulence 

Clear air turbulence (CAT) remains an unsolved problem for commercial aviation, with a 

significant annual cost to the industry. For this reason, we studied turbulence detection 

extensively, beginning with modeling. The horizontal-viewing spectral radiance calculated from 

the U.S. Standard Atmosphere is shown in Figure 1, for three flight altitudes, in the 

650 – 1650 cm-1 region. 
2 

Figure 1. The spectral radiance that would be observed viewing horizontally through the U.S. 

Standard Atmosphere for aircraft altitudes of 1.5, 4.5, and 9.5 km (reproduced from Figure 70, page 

86). 

The spectral radiance predictions in Figure 1 were calculated with the standard model known as 

the Line-by-Line Radiative Transfer Model (LBLRTM). The spectra are primarily caused by 

three atmospheric gases: carbon dioxide, ozone, and water vapor. The turbulence detection 

strategy was based on the idea that, when turbulence is present, both the temperature profile and 

the water vapor concentration will be disturbed. The temperature might be sensed in the CO2

spectrum, toward the left in Figure 1 whereas water vapor would be sensed with spectral lines 

toward the right. 

The curves in Figure 1 show that the atmosphere is semi-transparent in the 800 – 1200 cm-1

region, which offers the hope that a FLI might see radiance from far enough away to provide a 

useful warning time. This idea was investigated by calculating the relative contributions to the 

received radiance that came from various ranges. The results are shown in Figure 2. 
3 

Figure 2. Contributions to the total radiance from various spherical shells along the atmospheric 

path observed looking forward from an aircraft at three different altitudes: (a) 1.5 km (~850 mb), (b) 

4.5 km (~580 mb), and (c) 9.5 km (~280 mb) (reproduced from Figure 71, page 88). 

Figure 2 shows that significant fractions of the radiance come from ranges as great as 80 km, and 

that information from various ranges is contained in different parts of the spectrum, which 

suggests that there is great value in using a sensor with high spectral resolution. 

For cost reasons, all field experiments in the FLI project were ground based, not airborne, so the 

challenge was to find a scenario that could afford views of a disturbed atmosphere that might 

resemble CAT at flight altitudes. For this purpose, we decided to house the instruments at the 

Mountain Research Station (MRS), which is operated by the University of Colorado at an altitude 

of 9,500 feet near Ward, Colorado, during a wintertime mountain wave event. Because of a 

particularly severe turbulence event on 6 March 2004, a WRF model of the region was already 

available, and we developed simulation tools to predict the radiances that an imaging 

hyperspectral radiometer would have observed at the MRS for various viewing directions. The 

conclusion was that radiance variations within an image would be observable. Brightness 

Temperature Differences (BTDs) of several Kelvins were predicted for a realistic FOV and 

viewing direction, as shown by the typical results in Figure 3 below. The simulations were 

interpreted as showing that the FLI has the potential to observe the varying atmospheric state 

(temperature and H2O) associated with the mountain waves of the 6 March 2004 event. 
4 

Figure 3. Simulations of radiance values in a scene at 957 cm-1. (top) brightness temperature; 

(middle) deviations (in K) when vertical mean values are subtracted out; (bottom) deviations (in K) 

when azimuthal mean values are subtracted out (reproduced from Figure 145, page 128). 

GTRI brought two radiometric instruments to the MRS field campaign: a Telops Hyper-Cam and 

a D&P TurboFT spectro-radiometer. They were on a common mount and boresighted, so that 

they were always looking at the same part of the sky. The specifications of the Telops LWIR 

Hyper-Cam are given in Table 1. 

Table 1. Telops Hyper-Cam specifications 

Parameter Specification 

Spectral band 7.7 – 11.5 μm 

Number of pixels 320 x 256 

Spectral resolution 0.25 – 150 cm-1

IFOV 0.35 mrad 

Communication Ethernet 

Data transfer Cameralink 

Detector cooling Closed cycle 

The specifications of the D&P Instruments TurboFTTM Spectral Sensor are given in 

Table 2. The D&P is a spectro-radiometer based on a rotary Michelson interferometer, with a 

scan rate of 95 spectra per second. Its detectors are cooled with liquid nitrogen. Any number of 

spectra can be averaged together to decrease noise (we typically averaged 4 – 24). 

On-line channel  = 957 cm-1:

Max Variability

5 10 15 20 25

1

2

3

4

5

155 

160 

165 

170 

175 

5 10 15 20 25

1

2

3

4

5 -4 

-3 

-2 

-1 

0 

1 

2 

3 

4 

5 10 15 20

1

2

3

4

5 0.0 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.8 

Deviation from

the Vertical Mean

Brightness

Temperature

Azimuthal

Deviation
5 

Table 2. D&P TurboFT specifications 

Parameter Specification

Field of View 1.2 with 4-inch telescope (shown)

Detectors HgCdTe and InSb, LN2-cooled

Radiometric Calibration Internal hot/cold blackbody

Spectral Range 2.55 – 16 microns

Spectral Resolution 4, 8, and 16 cm-1

 (adjustable)

Spectral Accuracy +/- 2 cm-1

Spectral Radiance Units W/m2

 – micron – sr 

Scan Rate 95 spectra/second

Sampling Frequency 1.2 MHz

IR Signal Filter Bandwidth 512 kHz

A/D Converter 16 bits, +/- 10 V range

FFT Size 1024 – 32,768

Computer PC compatible, portable

Operating System Windows XPTM

Operating 15 – 35 C (59 – 95 F)

Sensor Head Size 12” L X 8” W X 8” H

Weight 10 lbs.

Power 120 VAC, 30 W maximum

Knowing that weather is very unpredictable with any type of lead time, the Science and 

Technology in Atmospheric Research Institute (STAR) team was watching for weather patterns 

that indicated high winds/turbulence. With only 3 days’ notice, the GTRI team went to MRS 

based on STAR’s prediction that high winds were likely. We conducted the field campaign 

during four days in November 2011. On November 17, lenticular clouds appeared over the 

mountains as shown in Figure 4. These clouds indicate high winds from the west, which are 

conducive to mountain waves. It is important to note that mountain waves themselves are a 

slowly varying standing wave pattern; they are not in themselves turbulent. However, turbulence 

is often associated with them. The test objectives were to observe the varying atmospheric state 

associated with the mountain waves using the Telops Hyper-Cam, and to observe the turbulence 

with the D&P TurboFT. (Previous experience in Boulder, Colorado in 2008 had shown features 

in time series of spectra recorded quickly that were interpreted as being indicative of turbulence.) 

Unfortunately, the D&P experienced problems in the field that resulted in unusable data. 
6 

Figure 4. Lenticular clouds over the Rocky Mountains on 17 November 2011. The photo was 

recorded in Nederland, Colorado, looking toward the west (reproduced from Figure 185, page 154). 

Despite an extensive analysis of the Hyper-Cam data, variations in the atmospheric state were not 

observed in the image data, either for angular variations across the scenes or for temporal 

variations in long series of images. For this reason post-test, we had a new WRF model developed 

by the National Center for Atmospheric Research (NCAR) for the conditions on 17 November 

2011, and we re-ran our simulations for the viewing directions and spectral resolutions that were 

actually used. This post analysis showed that the radiance variations were below the noise floor of 

the instrument, and that fact is reflected in the sensor requirements shown in the table at the end 

of this summary. Another finding of the field campaign was that the ground-based scenario (even 

at 9,500 feet elevation where models had showed it would be sufficient) is actually not favorable 

for observing atmospheric state variations. This limitation, in addition to the winds not being as 

strong as originally predicted by STAR, are probably key reasons that the FLI program has not 

yet demonstrated CAT detection. 

Several simulation and analysis tools were developed in the course of the turbulence efforts, 

including the FLI Forward Model (FLI-FM) which is much faster than LBLRTM, and several 

versions of a cloud clearing algorithm were implemented in Matlab for use in processing the 

Telops Hyper-Cam data. 

Runway Surface Conditions 

The concept of the runway condition investigations was that the spectral emissivities of various 

surfaces are significantly different in the LWIR region, as shown in Figure 5, and so an airborne 

spectral radiometer may be able to determine the runway surface conditions and thereby enable 

an estimate of stopping distance. For this reason, we made field measurements with the Telops 

Hyper-Cam on four different occasions, viewing natural ice and snow, as well as prepared scenes 

that included wet, dry, and iced surfaces. 
7 

Figure 5. Spectral emissivities of various runway surfaces (reproduced from Figure 314, page 248). 

The prepared scenes included a Labsphere InfraGold plate. This plate is roughened, so that it is 

approximately a Lambertian reflector, and it has an infrared reflectance of 97%. The purpose of 

the gold plate is to measure the downwelling radiance integrated over the entire sky dome, 

because that radiance is partially reflected into the direction of the Hyper-Cam by the various 

surfaces. 

We implemented algorithms to retrieve both surface temperature and emissivity maps of the 

imaged scene, and the results are shown in Figure 6 for 1000, 1100, and 1200 cm-1. The surface 

types are identified in the temperature map. The results shown in Figure 6 illustrate the power of 

hyperspectral imaging, but the analysis depended crucially on the radiance from the gold plate. 

Despite several attempts, were not successful in implementing a reliable algorithm to measure 

spectral emissivities without the gold plate in the scene. 
8 

Figure 6. Derived surface emissivity for three wavelengths and the derived surface skin temperature 

(lower right hand panel). The dark spots are missing data due to the fact that a local emissivity 

variance minimum was not found in the retrieval process (reproduced from Figure 321, page 258). 

Wake Vortices 

We first attempted to measure radiometric signatures of wake vortices behind landing aircraft 

during a previous phase of the FLI program, in Madison, Wisconsin (June 2008), using a Telops 

MWIR Hyper-Cam and the D&P TurboFT. Analysis of the data did not reveal any detectable 

radiance signal from wake vortices. However, in Madison the winds were gusting, and they most 

likely prevented vortices from forming, or quickly moved them out of the field of view. We 

conducted another series of measurements in Atlanta, Georgia during July through September 

2010, using the Telops LWIR Hyper-Cam. Atlanta is an ideal site, being the busiest airport in the 

world and only 20 minutes away from GTRI. These tests were meant as pre-tests to another larger 

data collection activity to make sure we knew what settings we needed for the main activity. We 

obtained permission to set up equipment in an unused lot directly in line with a runway, viewing 

aircraft landing from the east in calm conditions. We performed the measurements on several 

occasions. 

Despite a very extensive and thorough analysis of the resulting images data, we did not observe 

radiometric features of wake vortices. We did find exhaust trails immediately after an aircraft 

passed through the field of view, as shown in Figure 7. In this case, the Matched Filter algorithm 

was used, and the target spectrum was water vapor, which is a major combustion product. Many 

other algorithms and target spectra were used in the analysis, including the Adaptive Matched 

Filter and the Adaptive Cosine/Coherence Estimator. In cases where the vapor trail was observed, 

the background was modeled in a new way, and was described in an SPIE paper contained in 

Appendix C of this report. 
9 

Figure 7. The Matched Filter results on datacube 1452 (reproduced from Figure 52, page 78). 

New simulations later showed there is a small, but evident, temperature and moisture structure 

signature of the wake vortex disturbance, as shown in Figure 8. Wake vortex radiance simulations 

were then conducted using FLI-FM2 to estimate the magnitude, and spectral location, of the 

signal. 

Figure 8. The temperature (left panel) and moisture (right panel) distribution associated with the 

wake of a Boeing 747 jet aircraft (reproduced from Figure 31, page 63). 

Three regions were found where the wake vortex signal is a maximum; these are located at 700 

cm-1, 1250 cm-1, and 2000–2200 cm-1, as shown in Figure 9. These regions are all on the edge of, 

or outside of, the MWIR and LWIR atmospheric windows and hence commercial imagers do not 

exist for them. Previous simulations had shown that the MWIR and LWIR regions would be of 

use. 
10 

Figure 9. The standard deviation of the radiance spectrum (natural elevation angle dependence 

removed) as would be observed with a spectral resolution imaging spectrometer operating at a 

spectral resolution of 16 cm-1 at two different aircraft altitudes, 0 km and 6 km (reproduced from 

Figure 33, page 64). 

Other Hazards 

Other hazards investigated include volcanic ash, low visibility, and icing in flight. In addition, the 

distance to the hazard was also assessed. 

Volcanic Ash: Detection of volcanic ash has not been investigated by the FLI team since 

methods of modeling the movement of volcanic ash (PUFF model) and instruments for its 

detection (Airborne Hazards Detection System, Norwegian Institute for Atmospheric Research) 

were being commercialized at approximately the same time as this investigation. This report 

summarizes the literature and patents related to volcanic ash detection. 

Low Visibility: Based on the sensitivity studies in the previous Phase 1 FLI project, an imaging 

FLI spectrometer is expected to provide enhanced vision in the cockpit of an aircraft in low 

visibility conditions. An algorithm was demonstrated under a restricted set of aircraft altitude and 

atmospheric conditions. Data of varying visibility due to fog was collected in Atlanta on several 

occasions. However, after initial preview, it was determined that data collected in thirty-minute 

intervals on the same day would be better suited for the analysis. The project team was unable to 

collect this sporadic weather condition during the remainder of the project. 

Icing: In-flight icing could not be studied during this program, since no flight tests were 

conducted. However, previous investigations have shown that the indices of refraction for ice and 

water differ starting at 11 μm, therefore providing the opportunity for differentiation. That 

knowledge combined with the knowledge of the temperature of the cloud to diagnose the 
11 

existence of supercooled liquid water in clouds could be used to detect water droplets at subfreezing temperatures that cause airframe icing. 

Distance to Hazard: The ability to determine the distance to a hazard relies on the collection of 

high spectral resolution data (1 cm-1). Because we have shifted to lower spectral resolution data 

(16 cm-1) to try to detect the signal of the hazards, we have inherently lost the ability to 

determine the distance to the hazard using the CO2 line structure. However, an additional 

dedicated detector with two narrowband filters that can look at the amplitudes of the CO2 lines 

has been proposed as the method of determining the distance to the hazard should the FLI 

instrument be developed. 

Instrument Requirements 

We developed a set of instrument requirements for detecting the hazards listed above. These 

requirements are listed in Table 3. Each hazard is listed with its primary wavelength region of 

detection, resolution, sensitivity, and any caveats that might exist. 

Table 3. Instrument requirements for hazard detection (reproduced from Table 20, page 289). 

Hazard Wavelength 

Region 

Resolution Sensitivity Caveat 

Wake Vortices 5 – 8 μm Broadband 1 mK 5 – 8 μm is the opaque region 

of the atmosphere, and sensors 

do not exist for this range 

Mountain 

Wave 

Turbulence 

LWIR window 

region (8 – 12 μm) 

16 cm-1 50 mK Other wavelength regions 

cannot be excluded because 

they were not studied during 

this program 

Runway 

Surface 

Conditions 

LWIR window 

region (10 – 12 μm 

for ice/water 

discrimination) 

1 – 4 cm-1 Cannot omit gold plate in 

calculation of absolute 

emissivity) 

Runway 

Obscurations 

(animals) 

LWIR or MWIR Any thermal imager will work 

(does not require radiometric 

accuracy). 

Low Visibility LWIR 2.5 cm-1 

Distance to 

Hazard 

CO2 lines in 

MWIR, edges of 

LWIR window 

1 cm-1 

Volcanic Ash 6 – 13 μm Broadband 

microbolometer 

with narrowband 

(0.5 – 1.0 μm) 

filters 

50 mK This commercialized 

instrument uses five cameras 

with narrowband filters at 

specified bands (including 

SO2) [1]. 

Icing 11 – 17 μm 4 cm-1 or less Differences in slope of the 

indices of refraction will 

indicate differences in 

ice/water. Use this knowledge 

in conjunction with knowledge 

of temperature to indicate 

supercooled liquid. 
12 

Table of Contents 

1 Background ........................................................................................................................... 39

2 Yearly Summaries ................................................................................................................. 42

2.1 Year 1 Summary ............................................................................................................ 42

2.2 Year 2 Summary ............................................................................................................ 44

2.3 Year 3 Summary ............................................................................................................ 45

3 Fast FLI Forward Radiative Transfer Model ......................................................................... 46

3.1 Atmospheric Profile Retrieval Algorithm ..................................................................... 46

3.2 Cloud Emissivity Modeling ........................................................................................... 47

3.2.1 Method ................................................................................................................... 47

3.2.2 Channel Selection .................................................................................................. 47

3.2.3 Results ................................................................................................................... 48

3.3 Line-by-Line Radiative Transfer Model Calculations ................................................... 50

3.3.1 Measurements for Model Verification .................................................................. 50

3.3.2 Fast Model Development ...................................................................................... 52

3.4 FLI-FM, Version 2 ........................................................................................................ 54

4 Wake Vortices ....................................................................................................................... 58

4.1 Wake Vortex Models ..................................................................................................... 59

4.2 Wake Vortex Measurements ......................................................................................... 71

4.3 Wake Vortex Analysis ................................................................................................... 75

4.3.1 Detection Algorithms for Entrained Exhaust Gases .............................................. 75

4.3.2 Principal Component Analysis .............................................................................. 82

4.3.3 Wake Vortex Data Analysis Conclusions ............................................................. 83

5 Mountain Wave Turbulence .................................................................................................. 84

5.1 Preliminary Tests and Data Analysis ............................................................................. 85
13 

5.1.1 Background............................................................................................................ 85

5.1.2 D&P Sky Measurements ....................................................................................... 89

5.1.3 D&P Data Analysis of Previously Collected Data ................................................ 92

5.1.3.1 Trend Removal .................................................................................................. 93

5.1.3.2 Fitting Models to the Radiance Power Spectra ................................................ 103

5.1.3.3 Looking for Turbulence in the Radiance Data ................................................ 108

5.1.3.4 Are We Seeing Turbulence? ............................................................................ 112

5.1.4 WRF Model: March 6, 2004 ............................................................................... 122

5.1.5 FLI-FM Simulations ............................................................................................ 123

5.1.5.1 Spectral Resolution Dependence ..................................................................... 124

5.1.5.2 Elevation Angle and Distance Dependence ..................................................... 125

5.2 Mountain Research Station Data Collection Activity ................................................. 140

5.2.1 MRS Data Collection Preparations ...................................................................... 140

5.2.2 MRS Data Acquisition ........................................................................................ 151

5.3 Mountain Research Station Data Analysis .................................................................. 157

5.3.1 Hyper-Cam/D&P Data Comparison .................................................................... 157

5.3.2 D&P Calibration Issues ....................................................................................... 157

5.3.3 Analysis of D&P Data ......................................................................................... 158

5.3.4 Investigation of “Amoeba” Pattern in Hyper-Cam Data ..................................... 178

5.3.5 Temporal Variability Analysis Theory ................................................................ 182

5.3.6 Bad Pixel Map Creation ...................................................................................... 186

5.3.7 Cloud Detection/Removal Algorithms ................................................................ 187

5.3.7.1 Cloud Clearing ................................................................................................. 188

5.3.7.2 Cloud Correcting ............................................................................................. 190

5.3.7.3 Cloud Detecting ............................................................................................... 192

5.3.8 Temporal Variability Analysis Results ................................................................ 197

5.3.9 Gas Detection Algorithm Analysis ...................................................................... 204
14 

5.3.10 Temporal Anomaly Detection Analysis .............................................................. 205

5.4 Mountain Research Station Data: Simulated vs. Observed ......................................... 214

5.4.1 LBLRTM Simulation Using Denver Radiosonde Data ....................................... 214

5.4.2 FLI-FM Simulation with March 6, 2004 WRF Model ........................................ 216

5.4.3 FLI-FM2 Simulations with November 17, 2011 WRF Model ............................ 220

5.4.3.1 New WRF Model: November 17, 2011 ........................................................... 220

5.4.3.2 Observer Geometry and Hyper-Cam FOV in WRF Data Cubes ..................... 240

5.4.3.3 The Local Standard Atmosphere ..................................................................... 241

5.4.3.4 Simulation Results ........................................................................................... 243

5.5 Why Were We Unable to Detect Turbulence? ............................................................ 247

6 Runway Surface Conditions and Obstructions .................................................................... 248

6.1 Governing Equations for Emissivity Determination ................................................... 249

6.1.1 Constraints on Emissivity Determination ............................................................ 251

6.1.2 In-Scene Atmospheric Compensation ................................................................. 252

6.2 Surface Emissivity Measurements............................................................................... 254

6.2.1 Staged Ice and Water: September 2010 ............................................................... 254

6.2.1.1 Hyper-Cam Radiance Filtering ........................................................................ 255

6.2.1.2 Diffuse Sky Radiance Estimation .................................................................... 256

6.2.1.3 Surface Temperature and Emissivity Determination ....................................... 256

6.2.2 Naturally Occurring Ice: January 2011 ............................................................... 258

6.2.3 MRS Emissivity Measurements: November 2011 .............................................. 261

6.2.4 Staged Ice and Water: Winter 2012 ..................................................................... 263

6.3 Surface Emissivity Retrieval Algorithm ...................................................................... 268

6.4 Surface Emissivity Conclusions .................................................................................. 274

6.5 Runway Obscuration ................................................................................................... 276

7 Low Visibility ...................................................................................................................... 278

7.1 Low Visibility Measurements ..................................................................................... 278
15 

8 Volcanic Ash ....................................................................................................................... 285

9 In-Flight Icing ...................................................................................................................... 285

10 Distance to the Hazard ..................................................................................................... 287

11 Defining a FLI Sensor Requirements .............................................................................. 287

11.1 Wake Vortices ............................................................................................................. 287

11.2 Mountain Wave Turbulence ........................................................................................ 288

11.3 Runway Surface Conditions ........................................................................................ 289

11.4 Requirement Summary ................................................................................................ 289

12 Conclusions ..................................................................................................................... 290

12.1 FLI Forward Model Updates ....................................................................................... 291

12.2 Wake Vortices ............................................................................................................. 292

12.3 Mountain Wave Turbulence ........................................................................................ 292

12.4 Runway Surface Conditions ........................................................................................ 293

12.5 Low Visibility .............................................................................................................. 294

12.6 In-Flight Icing .............................................................................................................. 294

12.7 Volcanic Ash ............................................................................................................... 294

12.8 Distance to Hazard ...................................................................................................... 294

12.9 Recommendations ....................................................................................................... 295

13 Conferences, Papers, and Presentations .......................................................................... 295

14 References ....................................................................................................................... 297

Table of Figures 

Figure 1. The spectral radiance that would be observed viewing horizontally through the U.S. 

Standard Atmosphere for aircraft altitudes of 1.5, 4.5, and 9.5 km (reproduced from Figure 70, 

page 85). .......................................................................................................................................... 2
16 

Figure 2. Contributions to the total radiance from various spherical shells along the atmospheric 

path observed looking forward from an aircraft at three different altitudes: (a) 1.5 km (~850 mb), 

(b) 4.5 km (~580 mb), and (c) 9.5 km (~280 mb) (reproduced from Figure 71, page 87). ............. 3

Figure 3. Simulations of radiance values in a scene at 957 cm-1. (top) brightness temperature; 

(middle) deviations (in K) when vertical mean values are subtracted out; (bottom) deviations (in 

K) when azimuthal mean values are subtracted out (reproduced from Figure 145, page 127). ...... 4

Figure 4. Lenticular clouds over the Rocky Mountains on 17 November 2011. The photo was 

recorded in Nederland, Colorado, looking toward the west (reproduced from Figure 185, page 

153). ................................................................................................................................................. 6

Figure 5. Spectral emissivities of various runway surfaces (reproduced from Figure 314, page 

247). ................................................................................................................................................. 7

Figure 6. Derived surface emissivity for three wavelengths and the derived surface skin 

temperature (lower right hand panel). The dark spots are missing data due to the fact that a local 

emissivity variance minimum was not found in the retrieval process (reproduced from Figure 321, 

page 257). ........................................................................................................................................ 8

Figure 7. The Matched Filter results on datacube 1452 (reproduced from Figure 52, page 77). .... 9

Figure 8. The temperature (left panel) and moisture (right panel) distribution associated with the 

wake of a Boeing 747 jet aircraft (reproduced from Figure 31, page 62). ...................................... 9

Figure 9. The standard deviation of the radiance spectrum (natural elevation angle dependence 

removed) as would be observed with a spectral resolution imaging spectrometer operating at a 

spectral resolution of 16 cm-1 at two different aircraft altitudes, 0 km and 6 km (reproduced from 

Figure 33, page 63). ....................................................................................................................... 10

Figure 10: Atmospheric profile retrievals of temperature and moisture using ground-based and 

satellite interferometer data obtained over NASA LaRC on August 11, 2009. A near simultaneous 

radiosonde observation is shown to validate the result. ................................................................ 46

Figure 11. Transmittance of the atmospheric layer from 65 to 400 mb (red line), channels selected 

for the cloud emissivity calculations (green dots), and first 10 observed NAST-I cloudy 

radiances, scaled to 0.01*mW/cm2

-cm-1-sr (black lines) as an example. ...................................... 48

Figure 12. Mean spectrum of the effective cloud emissivity ε

*

CLD(ν) (red line) and its variability 

(blue line), obtained by averaging over 6264 estimations from the NAST-I JAIVEx data, case 

April 27, 2007. Black lines are examples of the individual spectra for ε

*

CLD(ν), computed from the 

spectra shown in Figure 11. ........................................................................................................... 49

Figure 13. First 6 principal components for the effective cloud emissivity. ................................. 49
17 

Figure 14. The accuracy of the EOF-fit for the effective cloud emissivity as a function of a 

number of principal components used. .......................................................................................... 50

Figure 15: IR image of sky at 1075 cm-1 on June 24, 2010, before (left panel) and after (right 

panel) a bad pixel map is applied. The red box surrounds the pixels used in the analysis. (radiance 

units W/m2

cm-1sr) .......................................................................................................................... 51

Figure 16: Sky radiance (average of 255 pixels). ......................................................................... 51

Figure 17: A one-minute average of the sky showing the exponential falloff of air density with 

altitude. .......................................................................................................................................... 52

Figure 18: A time-height plot showing aerosol layers, but no clouds. .......................................... 52

Figure 19: Comparison of the Hyper-Cam observed radiance spectrum for June 24, 2010 with 

radiances calculated by FLI-FM and LBLRTM for two different spectral resolutions. ................ 53

Figure 20: The difference between calculated spectra and the Hyper-Cam observed radiance 

spectrum for the window 900-950 cm-1 region of the spectrum. ................................................... 54

Figure 21. Layer transmittance, τ(ν), for three indicated atmospheric layers; only every 20th point 

from the new look-up-table is shown for graphic purposes. ......................................................... 55

Figure 22. Temperature derivative of layer transmittance, δτ(ν)/δTBOT, for three atmospheric 

layers. Unit is K-1; only every 20th point from the new look-up-table is shown for graphic 

purposes. ........................................................................................................................................ 55

Figure 23. Water vapor derivative of layer transmittance, δτ(ν)/δ(lbQBOT), for three atmospheric 

layers. H2O unit is vmr; only every 20th point from the new look-up-table is shown for graphic 

purposes. ........................................................................................................................................ 56

Figure 24. Layer transmittances computed with FLI Forward Model, version 2. ......................... 57

Figure 25. Residuals (LBL – FLI) for the same atmospheric state in both brightness temperature 

(red) and radiance (blue) units. ..................................................................................................... 57

Figure 26. Residuals (LBL – FLI) for the same atmospheric state in radiance units only calculated 

from LBLRTM – FLI-FM version 1 (red) and LBLRTM – FLI-FM version 2 (blue). ................ 58

Figure 27: Temperature associated with a wake vortex produced at 1000 meters by an airbus-319 

aircraft at 20.0 seconds. The Cross Track Index is distance in meters. ......................................... 59

Figure 28. The maximum brightness temperature difference expected to be observed by the 

Hyper-Cam. ................................................................................................................................... 60

Figure 29: The sensitivity of ground-based Hyper-Cam spectral radiances to atmospheric 

temperature variations of 1 K within a 1-meter layer, as a function of distance from the 

instrument. Each curve shown represents a different spectral channel. ........................................ 61

Figure 30: TASS wake vortex radiance simulations for different relative humidity conditions. . 62
18 

Figure 31. The temperature (left panel) and moisture (right panel) distribution associated with the 

wake of a Boeing 747 jet aircraft. ................................................................................................. 63

Figure 32. The standard deviation of the radiance observed across the field of regard of an 

imaging spectrometer associated with the temperature and moisture disturbances shown in Figure 

31. .................................................................................................................................................. 63

Figure 33. The standard deviation of the radiance spectrum (natural elevation angle dependence 

removed) as would be observed with a spectral resolution imaging spectrometer operating at a 

spectral resolution of 16 cm-1 at two different aircraft altitudes, 0 km and 6 km. ......................... 64

Figure 34. Total mean temperature and water vapor vertical profiles from TASS data. ............... 65

Figure 35. Deviation of the mean temperature vertical profile at the moment t from the total mean 

profile (left panel), and temperature horizontal variability for each altitude level (right panel). .. 66

Figure 36. Deviation of the mean water vapor vertical profile at the moment t from the total mean 

profile (left panel), and water vapor horizontal variability for each altitude level (right panel). .. 66

Figure 37. TASS Referenced Atmosphere (black) obtained by merging actual TASS mean 

profiles (red) with the US Standard Atmosphere (green). ............................................................ 67

Figure 38. Brightness temperature spectrum for the central pixel (α = 0°, β = 30°), TASS 

Referenced Atmosphere. ............................................................................................................... 68

Figure 39. Radiance dependence on elevation angle R(β) – R(β = 30°), TASS Referenced 

Atmosphere, brightness temperature units. ................................................................................... 68

Figure 40. Statistics (mean and 1 σ) of the difference between wake vortex perturbed and 

background radiances, obtained by averaging over all pixels and time moments, brightness 

temperature units. .......................................................................................................................... 69

Figure 41. Evolution and magnitude of wake vortex signal as it would be seen by broadband 

instrument in the 2200 – 2400 cm-1 spectral region. ..................................................................... 69

Figure 42. Updated FOV for MW and LW wake vortex simulations, represented by red box. .... 70

Figure 43. Brightness temperature RMS difference between wake vortex perturbed and 

referenced atmospheric state for a broadband spectrometer with 50 cm-1 width channels. .......... 70

Figure 44. Wake vortex signal at LW, MW, and SW optimal bands. ........................................... 71

Figure 45: An aerial view of the runway and equipment location for wake vortex testing. ......... 72

Figure 46. The test setup just off runway 26R at Hartsfield-Jackson International Airport in 

Atlanta, GA. .................................................................................................................................. 73

Figure 47. The locations of the Hyper-Cam in July and in August. The red arrows indicate the 

Hyper-Cam’s viewing direction. ................................................................................................... 74

Figure 48. September 2010 ATL field test setup with Hyper-Cam. .............................................. 74
19 

Figure 49: Range Corrected Backscatter plot for the UV Lidar, July 23, 2010. ........................... 75

Figure 50. The visible image of the aircraft that passed through the FOV (left), and the radiance 

image of the same aircraft (right). ................................................................................................. 77

Figure 51. Average radiance of entire datacube 1452. .................................................................. 78

Figure 52. The MF results on datacube 1452. ............................................................................... 78

Figure 53. The AMF results on datacube 1452. ............................................................................ 78

Figure 54. The ACE results on datacube 1452. ............................................................................. 78

Figure 55. The MF results on datacube 1452, using only columns 1 – 90 as background in the 

calculation. .................................................................................................................................... 79

Figure 56. The AMF results on datacube 1452, using only columns 1 – 90 as background in the 

calculation. .................................................................................................................................... 79

Figure 57. The ACE results on datacube 1452, using only columns 1 – 90 as background in the 

calculation. .................................................................................................................................... 79

Figure 58. The radiance image of the datacube following the passage of the aircraft. ................. 80

Figure 59. The adaptive matched filter results 1 second after the passage of the aircraft. ............ 80

Figure 60. The adaptive matched filter results 5 seconds after the passage of the aircraft. .......... 80

Figure 61. The adaptive matched filter results 10 seconds after the passage of the aircraft. ........ 80

Figure 62. The adaptive matched filter results 15 seconds after the passage of the aircraft. ........ 81

Figure 63. The adaptive matched filter results 20 seconds after the passage of the aircraft. ........ 81

Figure 64. The radiance of the averaged plume pixels, plotted with the radiance of the averaged 

background pixels. ......................................................................................................................... 82

Figure 65. The difference of the plume and background. ............................................................. 82

Figure 66. PCA filtered difference image integrated over 980 to 1100 wavenumber range, 

calculated for a 1 cm-1 resolution datacube. .................................................................................. 83

Figure 67. PCA filtered difference image integrated over 1100 to 1350 wavenumber range, 

calculated for a 4 cm-1 resolution datacube. .................................................................................. 83

Figure 68. PCA filtered difference image integrated over 1100 to 1350 wavenumber range, 

calculated for a 16 cm-1 resolution datacube. ................................................................................ 83

Figure 69. Path viewed by a forward-looking optical instrument aboard an aircraft. ................... 86

Figure 70. The spectral radiance which would be observed by an FLI looking through the US 

Standard Atmosphere for aircraft altitudes of 1.5, 4.5, and 9.5 km. A Norton-Beer Weak 

Apodization was applied to interferograms simulated to a maximum optical path difference of 1.0 

cm (i.e. an unapodized spectral resolution of 0.5 cm-1). ................................................................ 86
20 

Figure 71. Contribution to the total radiance from various spherical shells along the atmospheric 

path observed looking forward from an aircraft at three different altitudes: (a) 1.5 km (~850 mb), 

(b) 4.5 km (~580 mb), and (c) 9.5 km (~280 mb). ........................................................................ 88

Figure 72. MODTRAN predicted path radiance for a slant path to space, from 2.9 km elevation, 

at five different elevation angles. .................................................................................................. 89

Figure 73. The D&P test setup for the five degree elevation sky measurements. ......................... 91

Figure 74. The D&P test setup for the zenith sky measurements. ................................................. 91

Figure 75. Radiance time series. .................................................................................................... 93

Figure 76. Same data as shown in Figure 75, after trend removal. ............................................... 94

Figure 77. Probability distribution (histogram) for the data shown in Figure 75. The red curve is a 

smoothed version of the underlying histogram, and the black curve is a Gaussian distribution fit 

to the histogram. ............................................................................................................................ 94

Figure 78. Probability distribution for the data shown in Figure 76, i.e. the trend-removed data. 94

Figure 79. Correlation functions with and without trend removal. ............................................... 95

Figure 80. Black curve is radiance for wavenumber 961; Red curve is fitted linear trend; Blue 

curve is fitted linear trend with Gaussian noise (mean and std. deviation from radiance data). ... 96

Figure 81. Black and red curves are as seen in Figure 79; blue curve is the correlation function for 

the simulated data (blue curve) from Figure 80. ........................................................................... 96

Figure 82. Power spectrum of radiance time series (black). The blue curve is the fit of a quadratic 

function plus noise to the data, and the red curve illustrates using a von Karman model plus noise.

 ....................................................................................................................................................... 97

Figure 83. Same as Figure 82, but with trend-removed radiance data. ......................................... 97

Figure 84. The effect of trend removal on the power spectra. Wavenumber 961 shown. ............. 98

Figure 85. The effect of trend removal on the power spectra. Wavenumber 2354 shown. (Note 

that this is from a different day as the data shown above.) ........................................................... 98

Figure 86. Probability plot for non-trend removed data used in Figure 85. .................................. 98

Figure 87. Probability plot for trend removed data used in Figure 85. ......................................... 99

Figure 88. Original radiance data showing larger-scale sinusoidal trend. ..................................... 99

Figure 89. De-trended radiance data showing residual, smaller-scale sinusoidal trend. (Using 

quadratic trend removal, as above.) ............................................................................................. 100

Figure 90. Using Fourier method for trend removal. Red curve is the original data as seen in 

Figure 88; black curve is trend from Fourier method. ................................................................. 100

Figure 91. De-trended data – difference between red and black curves from Figure 90. ............ 101

Figure 92. Spectra from original (black curve) and Fourier de-trended data (red curve). ........... 101
21 

Figure 93. Correlation functions before and after quadratic trend removal, for the data shown in 

Figure 88 (original) and Figure 89 (de-trended). ......................................................................... 101

Figure 94. Correlation function for a cosine trend ...................................................................... 102

Figure 95. Same as in Figure 82, but using the 10th to last spectral points in fitting both models.

 ..................................................................................................................................................... 104

Figure 96. Same as in Figure 82, but using the 2nd to last spectral points in fitting both models.

 ..................................................................................................................................................... 104

Figure 97. Same as in Figure 83 (de-trended data), but using the 4th through last points in the fits.

 ..................................................................................................................................................... 105

Figure 98. Same as in Figure 83, but using the 10th to last points in the fits. .............................. 105

Figure 99. Model fits using the 4th through last points for the von Karman, and 10th through last 

for the quadratic. .......................................................................................................................... 106

Figure 100. Same as Figure 99, but using the 2nd through last points for the von Karman fit. ... 106

Figure 101. Same as Figure 99, but using the 4th through last points for both model fits. ......... 106

Figure 102. Same as Figure 99, but for the de-trended data. ....................................................... 107

Figure 103. Spectrum and fits for the data shown in Figure 88 (large sinusoidal trend). Fit points 

are 10th through last for both models. .......................................................................................... 107

Figure 104. Same as in Figure 103 but for de-trended data. Fit points were 10th through last for 

both models. ................................................................................................................................ 108

Figure 105. Correlation function at three different lags, for all wavenumbers, for de-trended data.

 ..................................................................................................................................................... 109

Figure 106. Same as Figure 105, but for the original data. ......................................................... 109

Figure 107. Correlation plot for the case with the large sinusoidal trends (cf. Figure 88). ......... 109

Figure 108. Same as Figure 107, but for the (quadratic) de-trended data. .................................. 110

Figure 109. Correlation plot for all lags and all wavenumbers. The color scale uses hot colors for 

positive values and cool colors for negative ones. ...................................................................... 111

Figure 110. Same as Figure 109, but for de-trended data. ........................................................... 111

Figure 111. Same as in Figure 110, but only for the first 50 data samples (approximately 15 

seconds). ...................................................................................................................................... 112

Figure 112. Correlation plot for 14 Jan 2008, Data Set 1, second 5000 points. .......................... 113

Figure 113. Correlation plot for 14 Jan 2008, Data Set 2. ........................................................... 113

Figure 114. Correlation plot from 15 Jan 2008, Data Set 1, second 5000 points. ....................... 113

Figure 115. Correlation plot from 15 Jan 2008, Data Set 1, third 5000 points ........................... 114

Figure 116. Correlation plot from 15 Jan 2008, Data Set 2, first 5000 points. ........................... 114
22 

Figure 117. Correlation plot from 15 Jan 2008, Data Set 2, second 5000 points. ....................... 114

Figure 118. Correlation plot from 17 Jan 2008, Data Set 1, first 5000 points. ........................... 115

Figure 119. Correlation plot from 17 Jan 2008, Data Set 3. ........................................................ 115

Figure 120. Power spectrum and fits for wavenumber 1504 from 14 Jan 2008, Data Set 1, 2nd

5000 points. ................................................................................................................................. 116

Figure 121. Power spectrum and fits for wavenumber 2355 from 14 Jan 2008, Data Set 1, 2nd

5000 points. ................................................................................................................................. 116

Figure 122. Power spectrum and fits for wavenumber 1520 from 14 Jan 2008, Data Set 2, 1st 5000 

points. .......................................................................................................................................... 116

Figure 123. Power spectrum and fits for wavenumber 961 from 15 Jan 2008, Data Set 1, 2nd 5000 

points. .......................................................................................................................................... 118

Figure 124. Power spectrum and fits for wavenumber 1504 from 15 Jan 2008, Data Set 1, 2nd

5000 points. ................................................................................................................................. 118

Figure 125. Power spectrum and fits for wavenumber 2355 from 15 Jan 2008, Data Set 1, 2nd

5000 points. ................................................................................................................................. 118

Figure 126. Power spectrum and fits for wavenumber 961 from 15 Jan 2008, Data Set 1, 3rd 5000 

points. .......................................................................................................................................... 119

Figure 127. Power spectrum and fits for wavenumber 1504 from 15 Jan 2008, Data Set 1, 3rd

5000 points. ................................................................................................................................. 119

Figure 128. Power spectrum and fits for wavenumber 2355 from 15 Jan 2008, Data Set 1, 3rd

5000 points. ................................................................................................................................. 119

Figure 129. Power spectrum and fits for wavenumber 1504 from 15 Jan 2008, Data Set 2, 1st 5000 

points. .......................................................................................................................................... 120

Figure 130. Power spectrum and fits for wavenumber 2355 from 15 Jan 2008, Data Set 2, 1st 5000 

points. .......................................................................................................................................... 120

Figure 131. Power spectrum and fits for wavenumber 1504 from 15 Jan 2008, Data Set 2, 2nd

5000 points. ................................................................................................................................. 120

Figure 132. Power spectrum and fits for wavenumber 2354 from 15 Jan 2008, Data Set 2, 2nd

5000 points. ................................................................................................................................. 121

Figure 133. Power spectrum and fits for wavenumber 1504 from 17 Jan 2008, Data Set 1, 1st 5000 

points. .......................................................................................................................................... 121

Figure 134. Power spectrum and fits for wavenumber 2354 from 17 Jan 2008, Data Set 1, 1st 5000 

points. .......................................................................................................................................... 121

Figure 135. Power spectrum and fits for wavenumber 1504 from 17 Jan 2008, Data Set 3. ...... 122
23 

Figure 136. Power spectrum and fits for wavenumber 2354 from 17 Jan 2008, Data Set 3. ...... 122

Figure 137. Horizontal (a) and vertical (b) cross-sections of vertical velocity and H2O 

respectively for the lee wave turbulence, March 6, 2004. ........................................................... 123

Figure 138. Spectral resolution dependence on lee-wave turbulence signal for a northward 

viewing Hyper-Cam. Lower right hand panel shows an image of the turbulence as would be 

viewed at 835 cm-1 by the Hyper-Cam operated at the lowest spectral resolution of 16 cm-1. ... 124

Figure 139. Spectral resolution dependence on lee-wave turbulence signal for a westward viewing 

Hyper-Cam. Lower right hand panel shows an image of the turbulence as would be viewed at 835 

cm-1 by a Hyper-Cam operated at the lowest spectral resolution of 16 cm-1. .............................. 125

Figure 140. Mean brightness temperature spectrum, averaged over all image pixels. ................ 126

Figure 141. Standard deviation from the mean brightness temperature spectrum. ..................... 126

Figure 142. Standard deviation of the quantity ΔBTVER (see text). The enlarged plot shows 

channels chosen for visualization. ............................................................................................... 127

Figure 143. Standard deviation of the quantity ΔBTHOR (see text). ............................................. 127

Figure 144. Standard deviation of ΔBTHOR versus standard deviation of ΔBTVER. ....................... 128

Figure 145. Imagery for the on-line 957 cm-1 spectral channel, which has maximum variability for 

both ΔBT. ..................................................................................................................................... 128

Figure 146. Imagery for the off-line channel 933 cm-1: the “regular” variability for both ΔBT. . 129

Figure 147. Layout of the viewing geometry fields of view for the simulations. ....................... 130

Figure 148. Statistics of the radiance signal at observer location no. 1. ...................................... 131

Figure 149. Statistics of the radiance signal at observer location no. 2. ...................................... 131

Figure 150. Statistics of the radiance signal at observer location no. 3. ...................................... 132

Figure 151. Statistics of the radiance signal at observer location no. 4. ...................................... 132

Figure 152. Statistics of the radiance signal at observer location no. 5. ...................................... 132

Figure 153. Statistics of the radiance signal at observer location no. 6. ...................................... 133

Figure 154. Statistics of the radiance signal at observer location no. 7. ...................................... 133

Figure 155. Modeled BTD image at observer location no. 1, α0 = 270° (West), β0 = 90°. ......... 134

Figure 156. Modeled BTD image at observer location no. 2, α0 = 270° (West), β0 = 60°. ......... 134

Figure 157. Modeled BTD image at observer location no. 3, α0 = 270° (West), β0 = 30°. ......... 134

Figure 158. Modeled BTD image at observer location no. 4 , α0 = 270° (West), β0 = 12°. ........ 135

Figure 159. Modeled BTD image at observer location no. 5, α0 = 180° (South), β0 = 60°. ........ 135

Figure 160. Modeled BTD image at observer location no. 6, α0 = 180° (South), β0 = 30°. ........ 135

Figure 161. Modeled BTD image at observer location no. 7, α0 = 180° (South), β0 = 12°. ........ 136
24 

Figure 162. Brightness temperature RMS difference over all image pixels when mean azimuthal 

brightness temperature is subtracted. .......................................................................................... 137

Figure 163. Brightness temperature peak-to-peak difference over all image pixels when mean 

azimuthal brightness temperature is subtracted. .......................................................................... 137

Figure 164. BTD from the azimuthal mean for ν = 826 cm-1 and β0 = 90°. ................................ 138

Figure 165. BTD from the azimuthal mean for ν = 1098 cm-1 and β0 = 60°. .............................. 138

Figure 166. BTD from the azimuthal mean for ν = 826 cm-1 and β0 = 45°. ................................. 139

Figure 167. BTD from the azimuthal mean for ν = 826 cm-1 and β0 = 30°. ................................ 139

Figure 168. BTD from the azimuthal mean for ν = 826 cm-1 and β0 = 15°. ................................ 139

Figure 169. Representation of test location at MRS looking at atmosphere over DEN. ............. 140

Figure 170. Mountain Research Station Tundra Site (left) and Map from Boulder, CO (right). 141

Figure 171. Rooftop platform on base camp building and access ladder/stairs. ......................... 141

Figure 172. Pike’s Peak, at a distance of 86 miles from the MRS. ............................................. 142

Figure 173. The snow vehicle at the C1 site. ............................................................................... 142

Figure 174. View to the west from clearing, Kiowa Peak (13,156 ft. elevation). ....................... 143

Figure 175. View to the east from clearing. ................................................................................ 143

Figure 176. Leanne West and Kurt Chowanski, Kiowa Peak ~6 miles to the west. ................... 144

Figure 177. UAL 757 reports over a 24 hour period. .................................................................. 145

Figure 178. DAL 737 reports over a 24 hour period. .................................................................. 145

Figure 179. SolidWorks illustration of the D&P instrument installed in its shelter with the heater 

(blue box). ................................................................................................................................... 147

Figure 180. Scale drawing of radiometric instruments on an over-and-under mount. ................ 148

Figure 181. A SolidWorks representation of the mount with the Hyper-Cam and D&P installed.

 ..................................................................................................................................................... 148

Figure 182. The final mount for the D&P and Hyper-Cam. ........................................................ 149

Figure 183. The D&P temperature readings during the time of the test. ..................................... 150

Figure 184. Instrument set-up looking out the open window of the MRS dining hall (left panel), 

and instruments looking ESE toward Denver (right panel). ....................................................... 151

Figure 185. Lenticular clouds, Thursday November 17, 2011. ................................................... 154

Figure 186. Aqua MODIS water vapor channel data; November 17, 2011; mountain waves 

visible. Band 27 (6.535 – 6.895 micron). .................................................................................... 154

Figure 187. PIREPs data from November 16, 2011; moderate turbulence reported. .................. 155
25 

Figure 188. GOES water vapor channel (top panel), experimental downslope wind product 

(middle panel), and model wind at 500 hPa (bottom panel), November 18, 2011. Colorbar scales 

and units for each are beneath each subfigure. ............................................................................ 156

Figure 189. Telops radiance (1 cm-1 resolution) vs. D&P radiance (4 cm-1 resolution). ............. 157

Figure 190. D&P interferograms. The green interferogram exhibits waviness about zero OPD that 

is seen in all even-numbered interferograms. .............................................................................. 158

Figure 191. Example of spikes in the radiance time series (ν = 936 cm-1). ................................. 159

Figure 192. Same as previous figure, but for ν = 1501 cm-1. ...................................................... 160

Figure 193. Sample-to-sample differences in the radiance time series. The vertical axis index 

corresponds to every fifth wavenumber index for the first 300 wavenumbers; the horizontal axis 

index corresponds to the sample number index in the time series. ............................................. 160

Figure 194. Same as previous figure, but the vertical axis index corresponds to every tenth 

wavenumber index for the first 1000 wavenumbers; the horizontal axis index corresponds to the 

sample number index in the time series. ..................................................................................... 161

Figure 195. Spike removal example. ........................................................................................... 161

Figure 196. Time gaps as a function of sample number. ............................................................. 162

Figure 197. Same as previous figure, but linear vertical scale – and limited to zero to one. ...... 162

Figure 198. Same as the previous figure, but zoomed in on samples 65000 to 70000. ............... 163

Figure 199. Same as previous figure, but zoomed in from 67200 to 67600. ............................... 163

Figure 200. Temporal versus radiance differences. ..................................................................... 164

Figure 201. Sample-to-sample temporal differences for the sampling period 80400 to 85200. . 165

Figure 202. Histogram of sample-to-sample time differences for samples 80400 to 85200. ...... 165

Figure 203. Same as Figure 202, but for the sample range 37500 to 46000. .............................. 166

Figure 204. Sample-to-sample temporal differences, showing “local” discontinuities in the 

sampling rate. Note that the predominant discontinuity is two samples long for samples in the 

80400 to 80500 period. ................................................................................................................ 166

Figure 205. Same as in Figure 204 but for the sample range 83000 to 83100. Note the much larger 

discontinuities in sampling rates, as well as the three-point discontinuities. .............................. 167

Figure 206. Same as in Figure 204, but showing a mixture of two- and three-point discontinuities 

in the sampling rate. Sample interval is 84750 to 84850. ............................................................ 167

Figure 207. Sample-to-sample temporal differences (offset by -0.25 sec) in black, with sample-tosample data difference values in red. Note the repeated data values are the zeros at the beginning 

of each discontinuity in sampling rate. The sample range is 80400 to 80450. ............................ 168
26 

Figure 208. Same as Figure 207, except that in this case the temporal discontinuities are not 

associated with repeated values. The sample range is 83000 to 83050. (Offset for sample-tosample temporal differences is -0.4 in this case.) ........................................................................ 168

Figure 209. Same as Figure 207, but for a three-point discontinuity case. The sample range is 

84800 to 84850. (Offset for sample-to-sample temporal differences is -0.42 in this case.) ........ 169

Figure 210. Log-histogram of the number of samples between repeated data values. ................ 169

Figure 211. Lomb-Scargle periodogram (red) and standard Fourier periodogram (black) the 

sample interval 80400 to 80900, ν = 913 cm-1. The Lomb-Scargle periodogram has been 

oversampled by a factor of two over the Fourier one. ................................................................. 171

Figure 212. Same as Figure 211, but for the sample interval 83000 to 83500. ........................... 171

Figure 213. Same as Figure 211, but for the sample interval 84000 to 84500. ........................... 172

Figure 214. Same as Figure 211, but for spectra averaged over the whole 80400 to 85200 range. 

(Note the change in scale from the un-averaged spectra.) ........................................................... 172

Figure 215. Sample-to-sample time differences and “fixed” time differences for the period 80400 

to 80500. (Abscissa is time in seconds from sample 80400.) ...................................................... 173

Figure 216. Same as Figure 215, but for the period 83000 to 83100. ......................................... 174

Figure 217. Same as Figure 215, but for the period 84750 to 84850. ......................................... 174

Figure 218. Spike- and trend-removed radiances and “fixed” radiances for the period 80400 to 

80500. (Abscissa is time in seconds from sample 80400.) .......................................................... 175

Figure 219. Same as Figure 218, but for the period 83000 to 83100. ......................................... 175

Figure 220. Same as Figure 218, except for the period 84750 to 84850. .................................... 176

Figure 221. Same as Figure 211, but for the “fixed” data (blue)................................................. 176

Figure 222. Same as Figure 212 but for the “fixed” data (blue).................................................. 177

Figure 223. Same as Figure 213, but for the “fixed” data (blue)................................................. 177

Figure 224. Same as Figure 214, but for the “fixed” data (blue)................................................. 178

Figure 225. Averaged Lomb (red) and Averaged “fixed” Lomb (blue) periodogram for the 

sampling interval 80400 to 85200. .............................................................................................. 178

Figure 226. Images of “amoeba” pattern in Hyper-Cam radiance for November 16 and 17, 2011.

 ..................................................................................................................................................... 179

Figure 227. Standard deviation for radiance perturbations averaged over 320 x 160 image pixels, 

separately for 14 data cubes processed (November 17, 2011, cubes 8940–8953). ..................... 180

Figure 228. Standard deviation for radiance perturbations for channel #4 (ν = 849.9 cm-1) that 

shows the highest variability across image pixels. ...................................................................... 180
27 

Figure 229. The interferogram image (left panel) and the radiance image (right panel) of the 

blackbody. ................................................................................................................................... 181

Figure 230. Mean vertical radiances; each successive channel shifted by 0.003 along the x-axis.

 ..................................................................................................................................................... 183

Figure 231. Standard deviation for vertically averaged radiances; each channel shifted by 0.001 

along the x-axis. .......................................................................................................................... 183

Figure 232. Spectral RMS difference over 320 x 160 image pixels for channels 4 – 38 and 14 

consecutive data cubes (from the bottom to the top; each successive curve is shifted by +1.0e-5 

W/m2

cm-1sr along the Y-axis). .................................................................................................... 184

Figure 233. Time variability (averaging over 14 observed data cubes) of the difference between 

observed and azimuthal (horizontal) mean radiance. Blue arrows indicate channels with maximal 

time variability, as shown in Figure 234 and Figure 235. ........................................................... 184

Figure 234. Time development (every odd data cube from 1 to 14) of the BTD deviations from 

the vertical mean for observed Telops channel # 5, ~863.18 cm-1. ............................................. 185

Figure 235. Time development (every odd data cube from 1 to 14) of the BTD deviation from the 

vertical mean for observed Telops channel # 28, ~1168.61 cm-1. ............................................... 185

Figure 236. Frequency of pixels marked as bad over seven processed runs. .............................. 186

Figure 237. Bad pixels with frequency = 2 over seven runs, caused mostly by the artifacts in the 

field of view. ................................................................................................................................ 187

Figure 238. Cloud-clearing results for June 23, 2010. The upper left panel shows the reference 

window region and cloud flag while the upper right, lower left, and lower right hand panels show 

the cloud-cleared results for the 800-816 cm-1 long-wave water vapor absorption region, the 

1025–1041 cm-1 ozone absorption region, and the 1284–1300 cm-1 shortwave water vapor region.

 ..................................................................................................................................................... 189

Figure 239. Cloud-clearing results for July 6-7, 2010. The upper left panel shows the reference 

window region and cloud flag while the upper right, lower left, and lower right hand panels show 

the cloud-cleared results for the 800–816 cm-1 long-wave water vapor absorption region, the 

1025–1041 cm-1 ozone absorption region, and the 1284–1300 cm-1 shortwave water vapor region.

 ..................................................................................................................................................... 190

Figure 240. Cloud-correction results for June 22, 2010. The upper left panel shows the reference 

window region and cloud flag while the upper right and lower left show the cloud-cleared and 

cloud-corrected results for the 1251–1283 cm-1 methane and 1210–1216 cm-1 water vapor 

absorption regions, respectively. The derived distance to cloud is shown in the lower right hand 

panel. ........................................................................................................................................... 192
28 

Figure 241. The temporal variability of the scene when cloudy pixels TCLD > 240 K are 

eliminated. Unit is brightness temperature, the same color scale 0–4 K is used for all panels. .. 193

Figure 242. The temporal variability of the scene for the transparent channel #8 (~903 cm-1) when 

different cloud brightness temperature threshold values are applied (215 K to 250K, increment 5 

K); November 17, Run 4. ............................................................................................................ 194

Figure 243. The temporal variability of the scene for the opaque channel #18 (~1035 cm-1) when 

different cloud brightness temperature threshold values are applied (260, 265, 270 K); November 

17, Run 4. Note that color scale is a factor of two smaller (0 – 2 K) than the previous figures. 

There are no pixels for TCLD ≤ 255 K. ......................................................................................... 194

Figure 244. The radiance from a sky pixel compared to the radiance from a cloud pixel. ......... 195

Figure 245. The variance in radiance at the beginning of the thirty minute run (top two images)

vs. the variance in radiance at the end of the thirty minute run (bottom two images). The images 

on the right have been masked by the cloud removal test. .......................................................... 196

Figure 246. The radiance from sky pixels at two elevations compared to the radiance of a cloud 

pixel. ............................................................................................................................................ 196

Figure 247. The variance in radiance at the beginning of the thirty minute run (top two images) 

vs. the variance in radiance at the end of the thirty minute run (bottom two images). The images 

on the right have been masked by the cloud removal test as in Figure 245, but with the additional 

constraint of checking the change in radiance between 903.02 cm-1 and 916.30 cm-1. ............... 197

Figure 248. Statistics for temporal variability of the signal obtained by averaging over all image 

pixels for each channel. Blue arrows indicate the two channels shown in the full-size images in 

Figure 249. ................................................................................................................................... 198

Figure 249. Full-size image of temporal variability for channels #4 (left panel) and #5 (right 

panel) that have maximum values of the signal. ......................................................................... 198

Figure 250. Radiance (W/m2

sr) imagery at 850 cm-1 from November 16, 2011, Run 1. Each frame 

(from top to bottom) was recorded at 18:13:45, 18:23:03, and 18:26:37 UTC, respectively. ..... 199

Figure 251. Spectral dependence of the temporal variability of the scene obtained by averaging 

over all image pixels for given channel, 7 runs. .......................................................................... 200

Figure 252. Image of temporal variability (radiance units) for channel #4, that have maximum 

values of the signal, 6 runs. ......................................................................................................... 200

Figure 253. Spectral dependence of the temporal variability of the scene obtained by averaging 

over all image pixels for a given channel, 8 runs total, radiance units. ....................................... 201

Figure 254. Spectral dependence of the temporal variability of the scene obtained by averaging 

over all image pixels for given channel, November 17, Run 4, brightness temperature units. ... 201
29 

Figure 255. Image of temporal variability for channel 4, radiance units (left panel) and brightness 

temperature units (right panel), November 17, Run 4. ................................................................ 202

Figure 256. Image of temporal variability for channel #23 in radiance units (left panel) and 

brightness temperature units (right panel), November 17, run4. ................................................ 202

Figure 257. From left to right: channel #5 from the first, middle, and last cubes of data, 

November 17, run 4. .................................................................................................................... 203

Figure 258. LWIR imagery from November 16 (a clear day) vs. November 17 (a cloudy day). 

Radiance in W/m2

sr, at 926.6 cm-1. ............................................................................................. 203

Figure 259. Denver, CO radiosonde data (November 18 2011 00Z). The dry slot is circled in red. 

Note the different scale on the altitude axis of the temperature plot. .......................................... 204

Figure 260. TSCD images for the first 15 data cubes, November 17, Run 8. ............................. 206

Figure 261. Temporal variability for November 17, Run 8 over all data cubes (~30 min), channel 

#4. The area of interest is marked with a red square. .................................................................. 206

Figure 262. Temporal variability for November 17, Run 8 over first 70 data cubes (~2 min), 

channel #4. ................................................................................................................................... 207

Figure 263. TSAD Result, November 17 Run8, frame 332 in time series. ................................. 207

Figure 264. TSCD result, November 17 Run 8, frame 332 in time series. .................................. 208

Figure 265. Radiance (W/m2

sr) at several wavenumbers from data cube 14408. ....................... 208

Figure 266. Average radiance of first datacube in Novenber 17, Run 5 set, units W/m2

cm-1sr. . 209

Figure 267. Radiance images at: Top: Frame 12 (956.14 cm-1) and Frame 17 (1022.53 cm-1), 

Middle: Frame 15 (995.97 cm-1) and Frame 31 (1208.45 cm-1), and Bottom: Frame 21 (1075.65 

cm-1) and Frame 22 (1088.93 cm-1). All at time step 100. Radiance units W/m2

cm-1sr. ............. 210

Figure 268. Same as Figure 267, but at time step 898. ................................................................ 210

Figure 269. Difference from the mean at: Top: Frame 12 (956.14 cm-1) and Frame 17 (1022.53 

cm-1), Middle: Frame 15 (995.97 cm-1) and Frame 31 (1208.45 cm-1), and Bottom: Frame 21 

(1075.65 cm-1) and Frame 22 (1088.93 cm-1). All at time step 100. Radiance units W/m2

cm-1sr.

 ..................................................................................................................................................... 211

Figure 270. Same as Figure 269, but at time step 898. ................................................................ 211

Figure 271. Ratio of radiances for bands: Top: Frame 12 (956.14 cm-1) and Frame 17 (1022.53 

cm-1), Middle: Frame 15 (995.97 cm-1) and Frame 31 (1208.45 cm-1), and Bottom: Frame 21 

(1075.65 cm-1) and Frame 22 (1088.93 cm-1). All at time step 100. Radiance units W/m2cm-1sr.

 ..................................................................................................................................................... 212

Figure 272. Same as Figure 271, but for time step 898. .............................................................. 212

Figure 273. Difference from mean of the band ratios shown in Figure 271. .............................. 213
30 

Figure 274. Difference from mean of the band ratios shown in Figure 272. .............................. 213

Figure 275. Interferogram image from the datacube at time step 898. The image is a “slice” of the 

interferogram at OPD position 100 (near center burst). The color scale is in raw intensity counts.

 ..................................................................................................................................................... 214

Figure 276. The Hyper-Cam observation geometry, November 17, 2011. ................................. 215

Figure 277. The atmospheric state, averaged over the two closest times, from the closest 

radiosonde launch site (Denver). ................................................................................................. 215

Figure 278. Comparison of the mean observed (black), LBLRTM-simulated (red), and the 

standard deviation (green) of the observed radiances around their means for ten elevation angles.

 ..................................................................................................................................................... 216

Figure 279. Mean radiance and its standard deviation for the central image pixel, obtained by 

averaging over 8 observer locations. ........................................................................................... 217

Figure 280. The same as Figure 279, but in brightness temperature units. ................................. 218

Figure 281. The temporal variability of simulated radiances (dark red curve with symbols) in 

comparison with that obtained from observations of several runs. Red arrows indicate channels 

for which full-size images of temporal variability are shown on the next figure. ....................... 219

Figure 282. Full-size images of the signal temporal variability, obtained from simulations 

(brightness temperature units, K). Note that color scale is slightly different for different channels.

 ..................................................................................................................................................... 219

Figure 283. Google Earth view of Colorado with PIREPs (yellow markers) and Hyper-Cam FOV 

(red lines) plotted. ....................................................................................................................... 220

Figure 284. In this map of turbulence at 4.1km elevation, the blue cross marks the MRS test 

location. ....................................................................................................................................... 222

Figure 285. The red East-West line indicates the line along which are modeled the atmospheric 

conditions in the following figures. ............................................................................................. 223

Figure 286. In this figure the blue cross marks the MRS test location and the elevation lines 

indicate the upper and lower elevation angles in the Hyper-Cam FOV of Run 5 collected on 

November 17th. ........................................................................................................................... 224

Figure 287. Potential temperature for the same East-West viewing line. ................................... 225

Figure 288. Relative humidity for the same East-West viewing line. ......................................... 226

Figure 289. Vertical wind velocity for the same East-West viewing line. .................................. 227

Figure 290. Predicted turbulence for same East-West viewing line. ........................................... 228

Figure 291. Predicted Turbulence Indicator for the same East-West viewing line. .................... 229

Figure 292. Logarithmic plot of Cn

2

 structure constant. .............................................................. 230
31 

Figure 293. Down-looking view of North-South line intersecting the MRS site. The following 

charts display predicted data along this longitude line. ............................................................... 231

Figure 294. Modeled air temperature and wind vectors. The blue lines again indicate the elevation 

angles bounding the Hyper-Cam field-of-view, this time for Run 8 looking SSE. ..................... 232

Figure 295. Potential temperature for the same North-South viewing line. ................................ 233

Figure 296. Relative humidity along the same North-South viewing line. ................................. 234

Figure 297. Vertical wind velocity along the same North-South viewing line. .......................... 235

Figure 298. Predicted turbulence along same North-South line through the test site. ................ 236

Figure 299. Predicted turbulence indicator for the same North-South view line. ....................... 237

Figure 300. Predicted atmospheric structure constant Cn

2

. .......................................................... 238

Figure 301. Dew Point Indicator for test location and time. ....................................................... 239

Figure 302. FOV in lat/lon coordinates (horizontal projection) for November 17, Run 5 

observations. ................................................................................................................................ 241

Figure 303. Horizontal Hyper-Cam FOV in WRF data cube boundary (left panel); vertical 

projection of Hyper-Cam FOV (right panel). Hyper-Cam FOV projections are in red; WRF data 

cube boundaries are in blue. ........................................................................................................ 241

Figure 304. The LSA temperature profile was set to be equal to averaged WRF temperature 

below 21 km (red line) and the US standard atmosphere above (black line). The green line shows 

WRF temperature variability (i.e., one standard deviation from the WRF mean). ..................... 242

Figure 305. Same as Figure 304, but for water vapor. ................................................................ 242

Figure 306. Temperature deviation from LSA for the three pixels. ............................................ 243

Figure 307. H2O deviation (vmr) from the LSA for the three pixels........................................... 243

Figure 308. RMS difference for deviation from unperturbed radiances (black and red) and for 

temporal variability (green), computed for 320 x 256 image pixels. The arrows indicate the 

channels shown in the following figures at full image resolution. .............................................. 244

Figure 309. Full-size image of R(t1) - R(t0), Hyper-Cam channel # 1. ........................................ 245

Figure 310. Full-size image of R(t1) - R(t0), Hyper-Cam channel # 20. ...................................... 245

Figure 311. Full-size image of R(t1) - R(t0), Hyper-Cam channel # 34. ...................................... 246

Figure 312. Full-size image of R(t4) - R(t1), Hyper-Cam channel # 19 (temporal variability!). . 246

Figure 313. Full-size image of R(t4) - R(t1), Hyper-Cam channel # 34 (temporal variability!). . 247

Figure 314. Spectral emissivity of runway surface materials and water, ice, and snow. ............ 248

Figure 315. Real index of refraction of water and ice in the infrared [33] [34]. ......................... 249

Figure 316. Imaginary index of refraction for water and ice in the infrared [33] [34]. ............... 249
32 

Figure 317. Percent change in material emissivity due to 1% change in gold standard signature. 

Note the average value of each material given as part of the figure legend. ............................... 252

Figure 318. Parking deck set-up on Georgia Tech campus (a); Elevation view of test set-up (b)

 ..................................................................................................................................................... 254

Figure 319. Average surface radiance spectra for pixels viewing the gold plate and average sky 

radiance for the same pixels viewing that viewed the gold plate. ............................................... 256

Figure 320. Spectral emissivity of different surface types (i.e. dry concrete, wet concrete, wet 

non-skid pavement, and dry non-skid pavement) retrieved from radiance spectra observed with 

the Hyper-Cam instrument .......................................................................................................... 257

Figure 321. Derived surface emissivity for three wavelengths and the derived surface skin 

temperature (lower right hand panel). The dark spots are missing data due to the fact that a local 

emissivity variance minimum was not found in the retrieval process. ........................................ 258

Figure 322. Parking lot south of the Baker Building, GTRI, on January 13, 2011. .................... 259

Figure 323. Radiances as measured by the Telops Hyper-Cam of surfaces on the Baker parking 

lot on January 13, 2011. .............................................................................................................. 259

Figure 324. The emissivities of the surfaces on the Baker parking lot. ....................................... 260

Figure 325. Average material emissivities. ................................................................................. 261

Figure 326. Hyper-Cam measurements for run “snow_scene_0000022941” taken on November 

19, 2011 at the Mountain Research Station in Colorado. The upper left panel shows the spectral 

mean brightness temperature whereas the remaining three panels show the emissivity for the 850, 

1000, and 1150 cm-1 channels, respectively. ............................................................................... 263

Figure 327. Cube average emissivity spectrum for snow and concrete. ..................................... 263

Figure 328. Emissivity test plan set-up. ...................................................................................... 264

Figure 329. Radiance image of parking deck and sky at 6:40 AM EST. The radiance image is an 

average over all bands (excepting the first and last 50), Radiance units W/cm-1m2

sr. ................ 265

Figure 330. Emissivity test plan set-up. The solid lines represent the FOV used in the November 

test; the dashed lines represent the FOV used in the December test. .......................................... 265

Figure 331. The averaged radiance datacube from the Dec 19 test. The ice is circled in yellow, 

and the water is circled in red. Radiance units W/m2

cm-1sr. ....................................................... 266

Figure 332. Maximum brightness temperature images for November 20 (left) and December 19 

(right) datasets, brightness temperature in Kelvin. ...................................................................... 267

Figure 333. Scatterplot of measured radiance vs. Planck calculated radiance for November 20 

(left) and December 19 (right) datasets. Data shown at 1259.9 and 1257.4 cm-1, respectively. 

Radiance units W/m2

cm-1sr. ........................................................................................................ 267
33 

Figure 334. Scatterplot of measured radiance vs. Planck calculated radiance for November 20 

(left) and December 19 (right) datasets. Data shown at 965.7 cm-1. Radiance in W/m2

cm-1sr. .. 268

Figure 335. Spectral emissivities of different surface types assumed for development of the 

training set. .................................................................................................................................. 269

Figure 336. Root Mean Square Error of the retrieved surface temperature for the 6,500 simulated 

runway surface conditions and for three different radiometric measurement noise levels; aircraft 

altitude 300 m. ............................................................................................................................. 270

Figure 337. Root Mean Square Error (RMSE) of the retrieved surface emissivity spectra for the 

6,500 simulated runway surface conditions, for three different radiometric measurement noise 

levels, and for three different aircraft altitudes............................................................................ 271

Figure 338. The difference in radiances for the same atmospheric state when the surface 

reflectance is specular or diffuse. ................................................................................................ 272

Figure 339. Mean and standard deviation for training set of radiances; aircraft altitude 300 m. 273

Figure 340. Downward radiance at the surface level. ................................................................. 273

Figure 341. Root Mean Square Error (RMSE) of the retrieved emissivity averaged over 500 

simulated runway surface conditions and for three different radiometric measurement noise 

levels; aircraft altitude 300 m. ..................................................................................................... 274

Figure 342. Spectral emissivity of materials representative of runway surfaces [38] [39]. ........ 275

Figure 343. Grey Wolf at night on pavement, Yellowstone National Park: visible (left) , and 

LWIR (right). .............................................................................................................................. 277

Figure 344: Wolf at night, on pavement. (visible images are not shown, they were black). ....... 277

Figure 345. LWIR image of deer at night at YNP in central region of composite image (visible 

image is black border). ................................................................................................................ 277

Figure 346: Elk at night in the distance, on grass. ....................................................................... 278

Figure 347: Elk in day in the distance, on grass. ......................................................................... 278

Figure 348. Nov. 30, 2010 Visible Image of Bank of America Building taken with the HyperCam. ............................................................................................................................................ 279

Figure 349. Google map view of Atlanta showing the AT&T Building relative to the 

measurement site. ........................................................................................................................ 280

Figure 350. The view from the Baker building fire escape on a clear day, January 28, 2011. ... 280

Figure 351. The view from the Baker building fire escape in reduced visibility conditions, 

January 18, 2011. ......................................................................................................................... 281
34 

Figure 352. IR image at 1076 cm-1 of the scene on January 18, 2011, visibility 2 miles (left 

panel). IR image at 1076 cm-1 of the scene on January 20, 2011, visibility 6 miles (middle panel). 

IR image at 1076 cm-1 of the scene on January 28, 2011, visibility 10 miles (right panel). ....... 281

Figure 353. NESR of selected sky pixels from January 18, 2011 (top left panel) January 20, 2011 

(top right panel) January 28, 2011 (bottom panel), Radiance units W/cm-1m2

sr. ....................... 282

Figure 354. Radiance comparison of building and sky at three visibilities. ................................ 282

Figure 355. A Google Map view of the scenes imaged on the February 1st test. ........................ 283

Figure 356. Visible image of Scene 1, looking East to 10th St. ................................................... 283

Figure 357. Visible image of Scene 2, looking South to parking lot. .......................................... 284

Figure 358. Visible image of Scene 3, looking Southeast to parking deck. ................................ 284

Figure 359. Telops Hyper-Cam NESR calculated from a uniform patch of sky. ........................ 300

Figure 360. Telops Hyper-Cam NESR at 1 cm-1 resolution. ....................................................... 302

Figure 361. Telops Hyper-Cam NESR at 4 cm-1 resolution. ....................................................... 302

Figure 362. Telops Hyper-Cam NESR at 16 cm-1 resolution. ..................................................... 303

Figure 363. Telops Hyper-Cam NESR at all resolutions. ........................................................... 303

Figure 364. D&P TurboFT NESR. .............................................................................................. 305

 
35 

Table of Tables 

Table 1. Telops Hyper-Cam specifications ..................................................................................... 4

Table 2. D&P TurboFT specifications ............................................................................................ 5

Table 3. Instrument requirements for hazard detection (reproduced from Table 20, page 286). .. 11

Table 4. FLIR Sensor System Specifications ................................................................................ 41

Table 5. Hyper-Cam instrument settings for June 24, 2010 sky test. ............................................ 50

Table 6. Hyper-Cam instrument settings for July 23, 2010 ATL field test. .................................. 73

Table 7. D&P instrument settings for April 29, 2011 sky test. ..................................................... 90

Table 8. Viewing geometry for the simulations. ......................................................................... 130

Table 9. D&P monitored temperatures (°C). ............................................................................... 150

Table 10. Data Collection Matrix, Atmospheric Observations. .................................................. 152

Table 11. Data Collection Matrix, Surface Emissivity Observations, Resolution = 1 cm-1 for all 

runs. ............................................................................................................................................. 153

Table 12. Telops monitored values during two blackbody and one scene measurement (all 

temperatures in Kelvin). .............................................................................................................. 181

Table 13. Simulation set-up details ............................................................................................. 240

Table 14. Hyper-Cam instrument settings for parking deck test. ................................................ 255

Table 15. Surface conditions measured during parking deck test. .............................................. 255

Table 16. Derived apparent sample temperatures. ...................................................................... 260

Table 17. Fluke Ti10 specifications. ........................................................................................... 276

Table 18. Hyper-Cam instrument settings for November 30, 2010 fog measurements. ............. 278

Table 19. Temperature and relative humidity readings for the low visibility data collection 

activities....................................................................................................................................... 280

Table 20. FLI Sensor Requirements for Hazard Detection ......................................................... 289

Table 21. Hyper-Cam measured noise characteristics. ................................................................ 304

Table 22. D&P TurboFT measured noise statistics at 4 cm-1 Resolution .................................... 305
36 

List of Acronyms, Abbreviations, and 

Symbols 

ACE 

AERI 

Adaptive Cosine/Coherence Estimator 

Atmospheric Emitted Radiance Interferometer 

AFIT 

AFM 

Air Force Institute of Technology 

AERI Fast Model 

AHDS Airborne Hazards Detection System 

AIAA American Institute of Aeronautics and Astronautics 

AIRIS 

AMF 

ASTER 

Airborne Infrared Imaging Spectroscopy (DRDC-Valcartier) 

Adaptive Matched Filter 

Advanced Spaceborne Thermal Emission and Reflection Radiometer 

ATL Hartsfield-Jackson International Airport, Atlanta, GA 

BOT Bottom (related to atmospheric layers) 

BT Brightness Temperature 

BTD Brightness Temperature Difference 

C1 Site at MRS where meteorological station is located 

CAPABLE Chemistry and Physics Atmospheric Boundary Layer Experiment 

CAT 

CMF 

Clear Air Turbulence 

Clutter Matched Filter 

CSIRO Commonwealth Scientific and Industrial Research Organisation 

CrIS Cross-track Infrared Sounder 

DEN Denver International Airport, Denver, CO 

DFT Discrete Fourier Transform 

DRDC Defence Research and Development Canada 

D&P Designs and Prototypes 

EDR Eddy Dissipation Rate 

EMI Electromagnetic Interference 

FFM Fast Forward Model 

FFT Fast Fourier Transform 

FLI Forward Looking Interferometer 

FLI-FM FLI Forward Model 

FLI-FM2 FLI Forward Model, version 2 
37 

FOR Field of Regard 

FOV Field of View 

FPA Focal Plane Array 

FTS Fourier Transform Spectrometry/Spectrometer 

GOES Geostationary Operational Environmental Satellite 

GTRI Georgia Tech Research Institute 

GUI Graphical User Interface 

HISE Hyperspectral Imaging and Sounding of the Environment 

HU Hampton University 

Hz Hertz 

IASI Infrared Atmospheric Sounding Interferometer 

IR 

JAIVEx 

JPL 

Infrared 

Joint Airborne IASI Validation Experiment 

Jet Propulsion Laboratory 

K 

LaRC 

Kelvin 

Langley Research Center 

LBLRTM Line-by-Line Radiative Transfer Model 

LW Long-wave 

LWIR 

MF 

Long-wave Infrared 

Matched Filter 

MODIS Moderate Resolution Imaging Spectroradiometer 

MRS Mountain Research Station 

MW Mid-wave 

MWIR Mid-wave Infrared 

NASA 

NESR 

National Aeronautics and Space Administration 

Noise Equivalent Spectral Radiance

NFOV Narrow Field of View

NPP National Polar-orbiting Partnership

OD Optical Depth

OPD Optical Path Difference

OSA Optical Society of America

PIREP Pilot Report

PUFF Volcanic Ash Tracking Model

R Radiance
38 

RH Relative Humidity

RMS Root Mean Square

RT 

SAM 

Radiative Transfer

Spectral Angle Mapper

SFO San Francisco International Airport, San Francisco, CA 

SPIE The International Society for Optics and Photonics

SSEC Space Science and Engineering Center 

STAR Science and Technology in Atmospheric Research Institute 

T Temperature 

TASS Terminal Area Simulation System 

TSAD Temporal-Spectral Anomaly Detector 

TSCD Temporal-Spectral Covariance Detector 

T&E Temperature and Emissivity 

UCAR 

UCSB 

University Corporation for Atmospheric Research 

University of California, Santa Barbara 

UTC Coordinated Universal Time 

Q Water Vapor 

WFOV Wide Field of View 

WRF 

maxOPD 

Weather Research and Forecasting 

Maximum Optical Path Difference 

netCDF Network Common Data Form 

rad Radian 

sr Steradian 

vmr Volume Mixing Ratio 

α Azimuth angle 

β

ε

ρ

ρ

Elevation angle 

Emissivity 

Normalized Correlation Function (D&P Analysis) 

Reflectivity (Emissivity Analysis) 

τ Transmittance 

ν Wavenumber (cm-1) 

ω Frequency 

σ Standard Deviation 
39 

Technical Report 

This Final Report under Task NNX09AR67A was prepared by personnel at the Georgia Tech 

Research Institute (GTRI), Hampton University (HU), and the Science and Technology in 

Atmospheric Research Institute (STAR). Work under this project supports NASA work 

objectives under the Integrated Intelligent Flight Deck Technologies Research Program, 

specifically “development of a comprehensive surveillance system design that enables robust 

detection of external hazards with sufficient time-to-alarm for safe maneuvering to avoid the 

hazards.” 

1 Background 

The only forward-looking airborne sensor that is currently certified for commercial aviation is the 

weather radar. As a result, if an external hazard is invisible to both radar and out-the-window 

human vision, it will go undetected, with potentially disastrous results. Hazards in this category 

include clear air turbulence (CAT), wake vortices, dry wind shear, volcanic ash, icing conditions, 

and runway hazards that go undetected due to reduced visibility. In this report, we describe 

modeling, testing, and analysis of a new sensor concept for detecting such invisible hazards in the 

terminal area and in flight: an infrared imaging system with high spectral resolution. In addition 

to its unique capabilities envisioned for specific in-flight hazards, such a sensor can also operate 

as a conventional infrared imager, which enhances human vision by providing night vision and by 

improving vision through obscurants such as fog and haze. The sensor’s capability to detect 

runway condition hazards (water, ice, snow, or objects) was also investigated. 

Efforts to detect aviation hazards with passive infrared (IR) sensors have a long history. In the 

late 1970s, Peter Kuhn developed an IR radiometer [4] that was flown and tested on three NASA 

aircraft, including the Galileo II flying laboratory during the 1979 Clear Air Turbulence Flight 

Test Program [5]. Kuhn's passive IR system was designed to detect CAT by sensing water vapor 

variability ahead of the aircraft, (in non-CAT conditions, the water vapor content was assumed to 

be constant). Several radiometric passbands were investigated, and the 26 – 35 μm 

(286 – 385 cm-1) region was claimed to be optimal. The radiometer had a single line of sight 

aimed at an angle of 11 degrees above the horizon. The signal was sampled at one Hz, and its 

standard deviation was calculated every six seconds as the aircraft flew toward the turbulent 

region. An increase in the standard deviation of the signal (“IR emission anomalies”) was taken 

as an indication of fluctuations in water vapor concentration ahead of the aircraft. Successful 

detections of CAT were reported at altitudes above 4.4 km, with a range of up to 60 km and alert 

times ranging from one minute at 4.4 km altitude increasing to four minutes at 11.3 km. The 

flight tests resulted in 194 turbulence encounters. The false-alarm rate was less than 8% in noncloud conditions, and only four percent of the turbulence encounters were not preceded by an 

alert [6]. 

In the mid-to-late 1980s, the Commonwealth Scientific and Industrial Research Organisation 

(CSIRO) Division of Atmospheric Research, located in Melbourne, Australia, developed an 

airborne passive IR radiometer that employed a two-band technique for detecting volcanic ash. 
40 

The researchers published several papers on both theoretical and experimental aspects of volcanic 

ash detection with their technique [7], [8], [9], [10], [11]. At that time, CSIRO expected the ash 

cloud detection distance to be 100 - 200 km and planned further research. Several different 

versions of the CSIRO radiometer were constructed and tested over the next few years, including 

a pod-mounted sensor that was flown in Tasmania and another version that was flown in Japan 

inside an aircraft cabin, looking out through an open port. 

Beginning in 1990, CSIRO held discussions with a number of airline and aerospace companies in 

the U.S. in an effort to commercialize the ash sensor. They were advised that a multi-function 

hazard warning system would receive far greater acceptance in the industry, so CSIRO 

investigated passive IR detection of other hazards including low-level clear air turbulence, sulfur 

dioxide (SO2), and sulfuric acid (H2SO4). The multi-function system was named the Airborne 

Hazard Detection System (AHDS). In March 1991, volcanic ash clouds were detected in groundbased tests in Japan near Mt. Sakurajima. Following these trials, CSIRO obtained patents, 

including US Patent No. 5,602,543, Detection System for Use in an Aircraft. 

The AHDS was an imaging radiometer with a 10 x 10 degree field of view. It incorporated five 

different IR passbands ranging from 6.8 to 11.9 μm for detecting volcanic ash, CAT, sulfur 

dioxide (SO2), and sulfuric acid (H2SO4). Warning time estimates were between 3 and 6 minutes. 

In October 1999, CSIRO presented the AHDS to a number of airline and aerospace companies 

with the hopes of commercializing the technology, but this effort was not successful. The system 

was eventually commercialized by the Norwegian Institute for Atmospheric Research [1]. 

In 1986, NASA and the FAA jointly set a timetable for developing and demonstrating a solution 

to the aviation hazard that was responsible for more than half of the U.S. commercial aviation 

fatalities of the previous decade: low-level windshear. Several detection technologies were 

investigated, including radar, Doppler lidar, and infrared radiometry. During the course of this 

effort, five NASA - FAA windshear conferences were held, with the most recent one in 

September 1993. During the windshear research effort, passive infrared (IR) detection was 

studied extensively by H. P. Adamson, who founded the Turbulence Prediction System (TPS) 

Company to commercialize IR sensors. Most of Adamson’s work is described in the proceedings 

of the NASA - FAA windshear conferences. 

In the early 1990s, the first observations of apparent infrared signatures associated with wake 

vortices were reported by Adamson and Morrison [12]. Under a NASA SBIR Phase 1 contract, 

Adamson used a scanning multi-band radiometer to make low-resolution images of wake 

vortices, using a two-band differential signal processing technique to maximize the system’s 

response at a desired range [13]. The vertically-pointing radiometer was positioned so that aircraft 

would fly over it as they landed on Runway 26L at the Denver Stapleton airport. The scanning 

technique provided low-resolution images with 2○

 x 2○ pixels arranged in four rows and seven 

columns (28 pixels total) for an 8○

 x 14○ composite field of view. In each pixel, the difference 

between the radiometric temperature and the ambient temperature was calculated in degrees 

Celsius. Images with no aircraft were included to show the noise level, which was +/- 1 ○

C. The 

time required to acquire one image was 30 to 60 seconds. 
41 

Two data sets were collected, during midday on May 12 and May 18, 1993 in calm conditions 

with temperatures of 24-25 ○

C and relative humidity 30-33%. Sky conditions were not given. 

The data sets show the infrared signatures associated with the wakes behind two different types of 

aircraft, a McDonnell-Douglas DC-10 and a Boeing 727. Because of the low temporal and spatial 

resolution of the measurements, the data were said to indicate the radiometric temperatures 

associated with aircraft wakes, but they do not show the structure or motion of vortices. 

The radiometric data were acquired from about thirty seconds before the DC-10 flew over the 

instrument until about ninety seconds after, for both types of aircraft. The vortex signature was 

normally a cold infrared wake at -2 to -3 ○

C below ambient, although the DC-10 produced a 

temperature difference of -7 ○

C on one occasion. The data indicated that the 727 had a warm 

wake on one occasion. 

In the early 1990's, Kuhn and Sinclair revisited the CAT problem and developed an 'S' layer 

model to describe the instabilities that lead to clear air turbulence and derived the specifications 

of a forward looking infrared (FLIR) sensor system capable of detecting the clear air turbulence 

hazard [6]. Kuhn and Sinclair’s specifications are given in Table 4. 

Table 4. FLIR Sensor System Specifications 

Wavelength 12.8 to 14.3 m 

Field of View 10 

Bandwidth 1.5 m 

Noise Equivalent Radiance 0.169x10-6 W/cm2

sr 

at a scanning rate of 1 frame/second 

Detector HeCdTe with 

Area = 1x10-4 cm2

/pixel 

Optics Area 125 mm2

Optics Transmission 0.32 

The ‘S’ layer model was developed from flight tests conducted by the Air Force during the High 

Altitude Clear Air Turbulence (HICAT) program. Kuhn and Sinclair claimed that large angle 

crossings of the horizontal temperature variations that comprise the 'S' Layers are necessary. 

Flight paths parallel to warm and cool troughs and ridges were said to yield insignificant 

temperature variations. 

More recently, during the AWIN (Aviation Weather INformation) program, GTRI investigators 

reviewed infrared radiometric techniques for hazard detection and recommended that future work 

include flight tests with a hyperspectral imager [14]. 
42 

The prior work on radiometric hazard sensors was reviewed in 2001 by Gimmestad, et al. who 

recommended that the atmospheric IR signatures of aviation hazards should be studied and 

documented on flight tests with a forward looking hyperspectral imaging system, and that the 

tests must include truth data [14]. The recommended flight tests were intended to provide basic 

data that would enable the development of optimized airborne sensors that would provide both 

high probability of detection and low false alarm rate. 

This long history of investigations into airborne IR radiometric detection of aviation hazards 

provided examples of apparent detections of both CAT and wind shear, as well as a data set that 

was interpreted as images of wake vortices. In addition, ground-based radiometric detection of 

volcanic ash clouds was demonstrated. However, all of the early work was performed with simple 

instruments that had low spectral, spatial, and radiometric resolution, and many of them 

employed a single line of site. The detections were generally not supported by truth data, and the 

atmospheric conditions that the radiometers were responding to were characterized poorly, if at 

all. Because the atmospheric state was not known, the performance of the radiometers as hazard 

sensors could not be checked with numerical simulations. These problems led Gimmestad et al. to 

recommend that flight tests should be conducted with a hyperspectral IR imaging system; that the 

flights should include truth data; and that the measurement effort should be supported by a 

parallel simulation effort. The study reported here is an outgrowth of those recommendations 

aimed specifically at assessing the feasibility of using a hyperspectral IR imaging system for 

aviation hazard detection. 

The Forward Looking Interferometer (FLI) concept is based on high-resolution infrared Fourier 

transform spectrometry technologies that have been developed for satellite remote sensing. To 

meet the needs of the commercial fleet, such a sensor should address multiple hazards to warrant 

the costs of development, certification, installation, training, and maintenance. In the first two 

phases of the FLI project, the instrument was evaluated for its potential to address multiple 

hazards during all phases of flight (takeoff, cruise, and landing) including wake vortices, low 

slant range visibility, dry wind shear, icing, clear air turbulence (CAT), and volcanic ash [15] [16] 

[17]. In this report, we summarize the results of the entire three-year project. 

2 Yearly Summaries 

The work discussed in this Final Report was part of a planned three-year project which included 

these major tasks: 

1. Develop improved sensor models and hazard metrics; 

2. Validate models and simulations by comparing predicted results to empirical data; and 

3. Conduct ground-based field measurements focused on mountain waves and CAT. 

Each of these three tasks is summarized by Year in the subsections below. Our technical 

accomplishments during the entire project are described in detail in the remainder of this report. 

2.1 Year 1 Summary 

This section of the report gives a brief summary of work completed in Year 1 [18]. 
43 

Runway Surface Conditions and Obstructions: In the first year several ground-based 

measurements were conducted. These ground-based measurements included iced and wet 

asphalt/concrete; runway surroundings such as terrain; and wildlife (with another LWIR imager). 

Low Visibility Measurements: Unfortunately in Year 1 reduced visibility conditions, such as 

fog and smoke, were not present after acquisition of the Telops Hyper-Cam; these measurements 

were completed in Year 2. 

ATL Wake Vortex Measurements: Year 1 included a small-scale ground-based data collection 

activity at Hartsfield-Jackson (ATL) with the Telops Hyper-Cam as a precursor to the full data 

collection for the detection of wake vortices that was originally planned for SFO in Year 3. The 

data collection activities were coordinated with personnel from ATL and Jacoby Development, 

the company that owns the property on which we were physically located during the data 

collection activity. Overall, three visits to ATL occurred in Years 1 and 2. 

Plan SFO Wake Vortex Measurements: Planning for the larger-scale data collection activity in 

SFO began; however, during the WakeNet Conference, it was learned that the SFO WindTracer 

Lidar was temporarily inoperable and that ATL had a WindTracer installed. Financially, 

operationally, and logistically it made sense to have the data collection activity in ATL. 

Therefore, the field test was officially moved from SFO to ATL. 

Model Validation: Several activities that underlie model validation and updates occurred during 

Year 1; this, however, is an ongoing process. These activities included wake vortex simulations 

for comparison with actual data, Line-by-Line Radiative Transfer Model (LBLRTM) 

calculations, and a Fast Forward Model Development. 

Wake Vortex Model Simulations: Hyper-Cam radiance simulations were conducted with wake 

vortex model data provided by Dr. Fred Proctor. We analyzed data for heavy aircraft typical of 

what was observed by the Hyper-Cam during measurements conducted at ATL. Also, we used the 

model simulations to simulate what a FLI would see from an aircraft at 1 km altitude (i.e. on 

takeoff or landing behind a heavy commercial aircraft). Simulations show small sensitivity to the 

temperature and water vapor variability produced by a wake vortex. 

Hyper-Cam Line-by-Line Radiative Transfer Model (LBLRTM) Calculations: LBLRTM 

calculations were performed using radiosonde measurements conducted from Peachtree City for 

comparisons with the sky viewing Hyper-Cam measurements made at GTRI on different 

occasions. These comparisons were used to evaluate the spectral and radiometric calibration 

accuracy of the Hyper-Cam measurements. 

A Fast Forward Model Development: A Fast Forward Model (FFM) was developed to produce 

rapid calculations of Hyper-Cam radiances from atmospheric state conditions as well as for use in 

an inverse radiative transfer model for retrieving atmospheric state parameters from Hyper-Cam 

measurements. Comparisons with time-consuming Line-by-Line calculations indicate an 

accuracy of the FFM calculations close to the observational errors of the Hyper-Cam 

observations. 
44 

Instrument Requirements: Development of instrument requirements was an ongoing task and 

was not specifically addressed in Year 1. 

2.2 Year 2 Summary 

This section of the report gives a brief summary of work completed in Year 2 [19]. 

Wake Vortices: Three separate field campaigns for the collection of wake vortex data were 

conducted at ATL during the summer and fall of 2010 (during Years 1 and 2) in preparation for a 

full-scale field campaign at either SFO or ATL (later determined to be ATL). The Telops 

Hyper-Cam was used to collect hyperspectral image data cubes. The main purpose of these tests 

was to test look angles, data collection parameters, and length of data collection episodes. 

Additionally it was hoped that wake vortices could readily be seen in the data based on previous 

modeling and analysis. 

Wake vortex data collected with the Telops Hyper-Cam at Hartsfield-Jackson International 

Airport (ATL) was analyzed by both the GTRI and Hampton University teams. Hyperspectral gas 

detection algorithms were used in an attempt to detect wake vortices by finding the exhaust gases 

they entrain. A methodology for the detection of wake vortices by utilizing these gas detection 

algorithms was presented; while the method does not detect vortices in the data, the directional 

averaging of sky radiance in the target/background modeling shows what appear to be vapor trails 

following the aircraft. The results were inconclusive as to whether the exhaust gases were 

actually being detected or some other disturbance was being seen. However, this method of 

breaking up the background demonstrates a novel way to detect mixed pixel targets against sky 

backgrounds; this method yielded results when other similar methods did not. The results warrant 

more study, since the possible detection of a turbulent effect, though not a vortex, is still relevant 

to the FLI program. This work was presented at SPIE in August 2011 [20]. 

Mountain Wave Turbulence: Turbulence associated with mountain waves came to the forefront 

of the remaining research effort for this program in place of wake vortices. Therefore, while 

always having been a part of the overall program, the main data collection field campaign was 

focused on detection of mountain waves. Analysis of previously collected hyperspectral data was 

re-visited and analyzed, new data for comparison was collected, and a site visit for Year 3’s field 

campaign near Boulder, CO was conducted. 

Runway Surface State and Contamination: As a secondary priority, runway surface state and 

contamination was studied with hyperspectral imaging. Emissivity images, obtained from an 

aircraft-mounted Forward Looking Interferometer (FLI), are intended to be used to alert the pilot 

of hazardous landing runway surface conditions and enable the determination of runway friction 

and associated stopping distance for a particular aircraft. This hazard detection technique is based 

on the fact that different surfaces have different emissivity spectra in the LWIR spectral region, 

where the FLI operates. Ground-based measurements taken during Year 2 included: ice; snow; 

wet/dry asphalt and concrete; and runway surroundings/hazards such as terrain, wildlife, and 

vehicles. Data was analyzed and a conference paper was presented at the Optical Society of 

America (OSA) Hyperspectral Imaging and Sounding of the Environment (HISE) conference in 

July 2011 [21]. 
45 

Low Visibility: As a secondary priority, reduced visibility conditions (such as fog) were 

exploited when they occurred to measure hyperspectral images of concrete/asphalt and buildings 

in these conditions. The main purpose of these measurements was to provide hyperspectral data 

to investigate a technique that was developed and reported in a previous phase of the FLI project. 

The technique is aimed at mitigating reduced visibility during landings in obscured conditions. 

2.3 Year 3 Summary 

This section of the report gives a brief summary of work completed in Year 3 of the current FLI 

project [22]. 

Fast FLI Forward Radiative Transfer Model: Updates to the Fast FLI Forward Radiative 

Transfer Model (FLI-FM) and verification by comparison with Line-by-Line Radiative Transfer 

Model (LBLRTM) were completed. Much of the modeling and simulation, regardless of the 

hazard under investigation, was accomplished using the updated FLI-FM2. 

Wake Vortices: An overview of infrared FTS for the detection of wake vortices was given 

during Year 3 in a brief summary from Taumi Daniels’ thesis [23]. Wake vortex models 

developed by Daniels using TASS and LBLRTM were described and are expanded to include 

FLI-FM2 simulations. 

Mountain Wave Turbulence: Mountain wave turbulence was the main hazard under 

investigation during Year 3. Mountain wave turbulence FLI-FM models and simulations were 

completed in order to provide guidance for test planning at the Mountain Research Station 

(MRS). The data collection activity, which was designed in an effort to capture turbulent signals 

in the atmosphere over the Colorado Rockies, was completed in November 2011. Analysis of 

MRS data included data correction (including calibration issues, bad pixel replacement, and cloud 

removal), temporal variability analysis, gas detection analysis, and temporal anomaly analysis. 

Runway Surface Conditions and Obstructions: The ability to detect the surface emissivity for 

various types of runway surface conditions with a FLI is important for the determination of 

runway friction and aircraft stopping distance. During Year 2, ground-based emissivity 

measurements were made of ice, snow, and wet/dry asphalt and concrete. The subsequent 

analysis of these measurements demonstrated a methodology for the detection of hazardous 

surface conditions; however, the ice and concrete measurements were set up (i.e. artificial manmade conditions, not natural ice). Only one opportunity for measurements of natural ice and snow 

occurred during Year 2, but it was not fresh (the snow had iced over). During Year 3, the 

opportunity arose to make the same measurements in a natural environment that was more 

representative of a hazardous runway condition. 

Volcanic Ash: Detection of volcanic ash has not been investigated by the FLI team, as methods 

of modeling the movement of volcanic ash (PUFF model) and instruments for its detection 

(Airborne Hazards Detection System, Norwegian Institute for Atmospheric Research) already 

exist. A summary of the relevant literature and patents was given. 

Distance to the Hazard: The ability to determine the distance to a hazard relies on the collection 

of high spectral resolution data (1 cm-1). Over the last year, we have shifted to lower spectral 
46 

resolution data (16 cm-1) to try to detect the signal of the hazards. As such, we have inherently 

lost the ability to determine the distance to the hazard using the CO2 line structure. However, an 

additional dedicated detector with two narrowband filters that can look at the amplitudes of the 

CO2 lines has been proposed. Note that these lines appear in the mid-wave infrared (MWIR). 

Detection has focused on the long-wave infrared (LWIR). Therefore a dedicated detector would 

be in the MWIR, while the hazard detection detector would be in the LWIR. 

3 Fast FLI Forward Radiative Transfer Model 

3.1 Atmospheric Profile Retrieval Algorithm 

Because of the small unique spectral radiance signals produced by different surface types, as well 

as those produced by wake vortices, a very accurate specification of natural atmosphere signals is 

required to validate the radiometric signatures of these surfaces and wake vortex phenomena. A 

new combined surface and satellite profile retrieval method was developed and tested with the 

upward-looking ground-based ASSIST (an AERI-like instrument) interferometer and downwardlooking MetOp satellite IASI (Infrared Atmospheric Sounding Interferometer) data. The groundbased radiometric measurements are optimal for profiling the lowest levels of the atmosphere, 

whereas the satellite measurements are optimal for profiling the free atmosphere thermodynamic 

structure. Figure 10 shows a result obtained at NASA Langley on August 11, 2009. An IASI-only 

atmospheric retrieval was used as an initial condition for the combined retrieval. The IASI-only 

retrieval is erroneously cold and dry below the 2-km (i.e. 800 hPa) level due to unaccounted-for 

cloud attenuation. The small-scale atmospheric boundary layer structure retrieved through the use 

of the ground-based interferometer data is noteworthy. This atmospheric retrieval technique was 

developed early in the program so that it could be applied to the surface emissivity and wake 

vortex measurement experiments. 

Figure 10: Atmospheric profile retrievals of temperature and moisture using ground-based and 

satellite interferometer data obtained over NASA LaRC on August 11, 2009. A near simultaneous 

radiosonde observation is shown to validate the result. 
47 

3.2 Cloud Emissivity Modeling 

A training set of data for predicting the effective cloud emissivity to be used for simulating the 

influence of clouds on FLI spectral radiance measurements was prepared. Taking into account the 

cloud contribution to the observed radiance is important for the specification of runway visibility 

from FLI measurements and determining aviation weather hazards (e.g. turbulence). Cloud 

removal algorithms for measured data will be described in Section 5.3.7. 

3.2.1 Method 

Actual NAST-I radiances, obtained during the JAIVEx validating campaign, were used to obtain 

estimated spectra of cloud emissivity. Heavy cloud conditions (the day of April 27, 2007) were 

chosen for this purpose. Two kinds of retrievals, called “clear” and “cloudy,” were produced for 

each input radiance. The “clear” retrieval corresponds to clear-sky conditions (no clouds), and the 

“cloudy” retrieval is obtained under a simulation of cloudy conditions. The latter one was reached 

by setting the temperature profile to be isothermal below the cloud altitude; the regression 

predicting matrix was derived from the training sets of atmospheric states and corresponding 

calculated radiances. The isothermal atmospheric state is equivalent to the opaque region for IR 

radiative transfer. Both kinds of retrievals were produced with a dual physical-statistical retrieval 

algorithm developed at HU. The level where “clear-trained” and “cloudy-trained” retrieved 

temperature profiles begin to split (looking from the top to the bottom of the atmosphere), 

provides an estimate of cloud top altitude, cloud fraction, and effective cloud emissivity. 

If RCLR(ν) and RCLD(ν) are radiances, computed for “clear-trained” and “cloudy-trained” retrieved 

atmospheric states respectively, then effective cloud emissivity ε

*

CLD(ν) can be derived from the 

following relation: 

(3.2.1)

Which results in the effective cloud emissivity, ε

*

CLD(ν): 

(3.2.2)

3.2.2 Channel Selection 

The selection of channels that are sensitive to the surface/cloud top emissivity signal was made 

using the following criteria. First, the transmittance τ(ν) of the atmospheric layer between 65 mb 

(observation altitude) and 400 mb (average cloud altitude retrieved for the JAIVEx April 27 case) 

was computed. Then, the channel selection was made by analyzing the τ(ν) spectrum so that the 

selected channel ν

*

 satisfied the following rules: 

τ(ν

*

) > 0.4; 

τ(ν

*

) is a point of a local maximum for τ(ν) within the 10 cm-1 wide surrounding 

spectral window; 

The estimated value of ε

*

CLD(ν) is in the 0 to 1 range. 

Missed channels were filled with a linear interpolation between selected ones in order to obtain a 

smooth spectrum of the effective cloud emissivity. As a final step, the channel mask used for 

retrieval was applied to the computed spectra of ε

*

CLD(ν); channels with ν in the 1000–1180 cm-1
48 

(O3 band), 1240–1310 cm-1 (NO2 and SO2 lines), and ν > 1600 cm-1 spectral regions were 

excluded from consideration. 

3.2.3 Results 

Figure 11 shows the analyzed transmittance spectrum τ(ν) and corresponding channels selected 

for the estimation of ε

*

CLD(ν). The first 10 observed NAST-I cloudy radiances are also shown (in 

units of 0.01* mW/cm2

cm-1sr to fit on the plot) in order to see the observed scene variability and 

the correspondence between transmittance/radiance line signatures. 

Figure 12 presents the mean effective cloud emissivity spectrum and its variability, obtained by 

averaging over 6264 NAST-I radiances for the April 27 case. The specific ε

*

CLD(ν) spectra, 

computed from the radiances in Figure 12, are also shown there. 

The first six Empirical Orthogonal Functions (EOFs) of the cloud emissivity, calculated by 

singular vector decomposition of the corresponding covariance matrix, are presented in 

Figure 13. It is worth noting that the first EOF is almost constant through all wavenumbers. This 

finding means that estimated ε

*

CLD(ν) spectra have very similar spectral shape (at least, to first 

order), and differ only by a constant bias from each other. 

Figure 14 shows the accuracy of the EOF-fit for the obtained spectra of the effective cloud 

emissivity. It can be seen that first 4-6 principal components are enough to reach the 0.01-level of 

accuracy for the representation of ε

*

CLD(ν).

Figure 11. Transmittance of the atmospheric layer from 65 to 400 mb (red line), channels selected for 

the cloud emissivity calculations (green dots), and first 10 observed NAST-I cloudy radiances, scaled 

to 0.01*mW/cm2

-cm-1-sr (black lines) as an example. 

w avenumber, cm -1

600 800 1000 1200 1400 1600

0.0

0.2

0.4

0.6

0.8

1.0

 (65mb:400m b)

Picked Channels

First 10 scaled R OBS
49 

Figure 12. Mean spectrum of the effective cloud emissivity ε

*

CLD(ν) (red line) and its variability (blue 

line), obtained by averaging over 6264 estimations from the NAST-I JAIVEx data, case April 27, 

2007. Black lines are examples of the individual spectra for ε

*

CLD(ν), computed from the spectra 

shown in Figure 11. 

Figure 13. First 6 principal components for the effective cloud emissivity. 

wavenumber, cm-1

600 800 1000 1200 1400 1600

0.0

0.2

0.4

0.6

0.8

1.0

First 10 CLD

Mean

1 Sigma

wavenumber, cm-1

600 800 1000 1200 1400 1600

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

EOF # 1

EOF # 2

EOF # 3

EOF # 4

EOF # 5

EOF # 6
50 

Figure 14. The accuracy of the EOF-fit for the effective cloud emissivity as a function of a number of 

principal components used. 

3.3 Line-by-Line Radiative Transfer Model Calculations 

3.3.1 Measurements for Model Verification 

The Hyper-Cam was set up on an optics table directly under the open lidar hatch on a clear day, 

June 24, 2010. The Hyper-Cam configuration was such that it was looking straight up at the sky, 

the resolution was set at 1 cm-1, and the integration time was set at 250 μs. The Hyper-Cam 

instrument settings are shown in Table 5. Thirty-one interferograms were averaged to produce 

every datacube, thus reducing the noise. This averaging was done on both the blackbodies and the 

sky measurements. Thirty-one was chosen because this was the upper limit. 

Table 5. Hyper-Cam instrument settings for June 24, 2010 sky test. 

Blackbody 1 set point 1.0°C 

Blackbody 2 set point 50.0°C 

Resolution 1 cm-1

Integration time 250μs 

FPA 100 x 100 

Interferograms averaged per measurement 31 

Acquisition time (for 31 interferograms) 12 minutes

Data acquisition commenced on June 24 at about 8:00 EDT, which is 1200 UTC, the time of the 

upper air data balloon launch at Peachtree City, which is south of Atlanta. The 355 nm aerosol 

lidar system was also operated so as to check for any sub-visual cirrus, which would affect the 

sky radiance and cause a difference between measured and modeled values, if such cirrus were 

present. 

The IR image of the sky at 1075 cm-1 is shown in Figure 15. The image in the left panel is the 

radiance image, and the image in right panel is the radiance image after a bad pixel map has been 

applied. A 17x15 window of 255 pixels was chosen for analysis, as shown by the red box in the 

right panel of Figure 15. 
51 

Figure 15: IR image of sky at 1075 cm-1 on June 24, 2010, before (left panel) and after (right panel) a 

bad pixel map is applied. The red box surrounds the pixels used in the analysis. (radiance units 

W/m2

cm-1sr) 

Note the ring that appears in the infrared images in Figure 15. This is due to the off-axis 

wavelength shift of the sensor. Because we only consider the center pixels for analysis we have 

not corrected for this effect. 

The average radiance of all the pixels in the red box in the right panel of Figure 15 is shown in 

Figure 16. 

Figure 16: Sky radiance (average of 255 pixels). 

The lidar data showed that the sky was in fact very clear. The range-corrected profile shown 

below, in Figure 17, is a one-minute average. The profile basically follows the exponential falloff 

of air density with altitude, with the exception of some small layers of aerosol in the 2500 – 4000 

meter range. There is no evidence of cirrus, which was our main concern. 
52 

Figure 17: A one-minute average of the sky showing the exponential falloff of air density with 

altitude. 

The lidar data can also be presented as a time-height plot, as shown in Figure 18. Again, faint 

aerosol layers can be discerned but no clouds (note that the time base is incorrect in this plot; the 

lidar was operated for approximately one hour). 

Figure 18: A time-height plot showing aerosol layers, but no clouds. 

3.3.2 Fast Model Development 

A fast model was developed to enable retrievals of atmospheric temperature and moisture from 

the Hyper-Cam measurements. The model uses the 0.5 spectral resolution AERI Fast Model 

(AFM), as its basis, with the result being converted to any of the Hyper-Cam’s selectable spectral 
53 

resolutions via a double FFT process. Specifically, the AERI (Atmospheric Emitted Radiance 

Interferometer) fast model apodized radiance spectrum is converted to an interferogram through 

an FFT of the radiance spectrum. Next, the interferogram is deapodized by dividing the 

interferogram by the AFM apodization function. Finally the unapodized interferogram is 

truncated at the maximum optical path difference associated with the spectral resolution of the 

Hyper-Cam observations. 

Figure 19 shows a comparison of the Hyper-Cam observed radiance spectrum for June 24, 2010 

with radiances calculated by FLI-FM and LBLRTM for two different spectral resolutions. The 

FLI-FM results appear to compare with the observed spectrum with the same degree of accuracy 

as the time-consuming line-by-line calculation. To illustrate this agreement further, Figure 20 

shows the difference between the calculated spectra and the Hyper-Cam observed radiance 

spectrum for the window 900-950 cm-1 region of the spectrum. Here it is seen that the spectrum 

calculated using FLI-FM is nearly as good as the LBLRTM calculation for the spectral resolution 

of the radiance observation (maxOPD = 0.6 cm). It is also noteworthy that the standard deviation 

between the LBLRTM calculation for the observed spectral resolution of 0.833 cm-1 (i.e. 

maxOPD = 0.6 cm) is significantly smaller than that for half observation spectral resolution (i.e. 

maxOPD = 0.3 cm), indicating that there is little self apodization being produced by any 

misalignment of the Hyper-Cam optics or the instrument’s finite field of view. 

Figure 19: Comparison of the Hyper-Cam observed radiance spectrum for June 24, 2010 with 

radiances calculated by FLI-FM and LBLRTM for two different spectral resolutions. 
54 

Figure 20: The difference between calculated spectra and the Hyper-Cam observed radiance 

spectrum for the window 900-950 cm-1 region of the spectrum. 

3.4 FLI-FM, Version 2 

The FLI Forward Model (FLI-FM) was updated to version 2 (FLI-FM2) during Year 3. The fast 

computations of FLI-radiance calculated with FLI-FM are based on the pre-calculated optical 

depth (OD) look-up-table. A newer version of the table was prepared in FLI-FM2, which 

implements the following improvements: 

1. The wavenumber range was extended to 625–2475 cm-1 (before it was equal 

to 650–1650 cm-1). 

2. The look-up-table is now computed for six atmospheric models: tropical, midlatitude 

summer, midlatitude winter, polar summer, polar winter, and the US standard atmosphere. It 

accounts for seasonal/latitudinal atmospheric state variations more accurately. Before it was 

completed only for the US standard atmosphere. 

3. Monochromatic optical depths pre-computed with LBLRTM (resolution 10-4 cm-1) have been 

converted into transmittances for each layer and then convolved with a double Fourier 

transform into resolution 10-2 cm-1 (FLI “monochromatic” resolution). Before, optical depths 

had been simply averaged (boxcar) to the lower resolution. 

4. The fact that the atmospheric state is defined on levels, but optical depths/transmittance are 

layer characteristics, results in the situation where varying one parameter (say, the 

temperature on some level) changes the optical characteristics of two layers: one below and 

one above of this level. The new table version includes both bottom and top Jacobians 

(derivatives) of the transmittance, τ(ν). Before, only one (bottom) derivative had been saved. 

The Jacobian is computed for temperature and water vapor at all levels (0–95 km). 
55 

Time-consuming LBLRTM calculations to implement these improvements were performed. 

Figure 21, Figure 22, and Figure 23 show transmittance and its temperature and water vapor 

derivatives for layers with boundary levels 0–1 km, 5–6 km, and 10–11 km, respectively, as an 

example of the updated version of the look-up table. These changes have been adopted and 

incorporated into the FLI Forward Model. 

Figure 21. Layer transmittance, τ(ν), for three indicated atmospheric layers; only every 20th point 

from the new look-up-table is shown for graphic purposes. 

Figure 22. Temperature derivative of layer transmittance, δτ(ν)/δTBOT, for three atmospheric layers. 

Unit is K-1; only every 20th point from the new look-up-table is shown for graphic purposes. 
56 

Figure 23. Water vapor derivative of layer transmittance, δτ(ν)/δ(lbQBOT), for three atmospheric 

layers. H2O unit is vmr; only every 20th point from the new look-up-table is shown for graphic 

purposes. 

The model updates lead to better performance of the FLI-FM in terms of its agreement with 

LBLRTM. Figure 24 shows transmittances computed with the updated FLI Forward Model for 

some of the atmospheric layers. Figure 25 presents the difference between spectra computed with 

LBLRTM and FLI-FM2 (abbreviated LBL and FLI, respectively, in the legend) in both 

brightness temperature and radiance units. One can note that the residual does not exceed 0.3 K 

through almost the entire wavenumber range, with the exception being the ozone band between 

1000 and 1100 cm-1. This exception may be explained by the fact that the ozone is a fixed gas in 

FLI-FM, i.e. its spectral dependence on the input temperature profile is not taken into account. 

Finally, Figure 26 presents a direct comparison of the residual differences between spectra 

calculated by LBLRTM and both FLI-FM and FLI-FM2 in radiance units. This figure shows the 

improved performance of FLI-FM2 quantitatively; note that residuals have decreased 

dramatically. It is also important to note the extended wavelength range; the red line in Figure 26 

ends at 1300 cm-1.

 
57 

Figure 24. Layer transmittances computed with FLI Forward Model, version 2. 

Figure 25. Residuals (LBL – FLI) for the same atmospheric state in both brightness temperature 

(red) and radiance (blue) units. 
58 

Figure 26. Residuals (LBL – FLI) for the same atmospheric state in radiance units only calculated 

from LBLRTM – FLI-FM version 1 (red) and LBLRTM – FLI-FM version 2 (blue).

4 Wake Vortices 

The formation and evolution of aircraft wake vortices was reviewed in the final report for an early 

phase of the FLI project [15]. Two potential detection mechanisms were described: 1) detection 

of temperature anomalies such as cool vortex cores; and 2) detection of entrained exhaust gasses 

such as water vapor, CO2, CO, NOx, and VOCs. 

The vortex parameters (circulation, core size, spacing, descent rate, and decay rate) depend on 

weather conditions as well as the type of aircraft, its weight, and its airspeed. In Phase 2 of the 

FLI program, the Burnham-Hallock (BH) model was used to model the physical characteristics of 

vortices [24]. The model calculations were performed over ranges of vortex parameters rather 

than for specific scenarios. The radiometric wake vortex model was updated to accommodate 

input parameters for specific cases in coordination with the broader wake vortex community. 

Data were collected at Atlanta’s Hartsfield-Jackson International Airport to investigate these 

mechanisms. Cool vortex cores were not detected, and new simulations have shown that the 

radiometric signatures of wake vortex temperature structures are quite small. The lack of 

detection validates the model, albeit without truth data. However, exhaust plumes have been 

detected in the hyperspectral imagery. 

Through both modeling and data analysis of images with vortices, we have already concluded 

that the signal of the wake vortices is too small to detect with the currently available Hyper-Cam 

instrument. The research team still believes it would be valuable to run the Hyper-Cam and other 

hyperspectral instrumentation next to a WindTracer Lidar at an airport facility if the opportunity 

ever arieses, because truth data was unavailable for all of the work to date. Therefore, it is not 

possible to know if or when wake vortices were within the instrument’s field of view. 
59 

4.1 Wake Vortex Models 

Hampton University created a Terminal Area Simulation System (TASS) Large Eddy Simulation 

run for an A319 case in level flight, but it did not include water vapor. This data set (3-D) was 

used at 5 time slices (10 second intervals) in conjunction with the FLI-FM model. Matlab code 

and Fortran code were written to perform these analyses. FLI spectra were simulated for a wake 

vortex condition produced by a numerical model for an Airbus-319 aircraft. Simulations were 

produced for level flight at 1000 m and with variations in temperature, moisture, and distance to 

the vortex from the aircraft. These simulations were used to provide an initial estimate of the 

radiance signal expected from the Hyper-Cam prior to any Hartsfield-Jackson International 

Airport (ATL) and San Francisco International Airport (SFO) field experiments viewing wake 

vortices. Figure 27 shows the temperature structure produced for the wake vortex as simulated by 

the NASA numerical model. As can be seen from this model there is significant temperature 

variation that may be detectable by a FLI instrument if the noise floor is low enough to detect the 

signal. The requirements for such an instrument are given in Section 11. 

 Figure 27: Temperature associated with a wake vortex produced at 1000 meters by an airbus-319 

aircraft at 20.0 seconds. The Cross Track Index is distance in meters. 

During Year 1, considerable discussion took place to prioritize wake vortex model simulations 

being performed by Dr. Fred Proctor and Taumi Daniels. There was already one simulation for 

the A319 aircraft, which is on the lighter side of heavy commercial aircraft. A decision was made 

to pursue simulation of a heavy aircraft typical of what was observed during the Hyper-Cam 

measurements to be conducted at ATL. After considering the data limitations available for the 

model runs, it was decided to perform the next simulation for a 747 heavy aircraft. It was also 

conveyed to Dr. Proctor that both temperature and water vapor are needed as output from the 

model in order to properly simulate the potential detection simulations capability of a FLI (or in 

the ATL case, Hyper-Cam) measurement. These updated models were used in the simulations 

described in this section. 
60 

Further simulations by Hampton University examine changes based on temperature differences 

across the wake vortex. For the A319 TASS simulation, a pair of wake vortices was generated in 

a 3-D space. Temperature and wind fields at ten second intervals were used as input to a FLI-like 

model. For the results below, 100 cases for the single pixel were simulated, each with the 

temperature decreasing by a fixed amount. All other parameters were held constant (namely water 

vapor mixing ratio, distance to vortex, and altitude of wake vortices and aircraft). Each of the 100 

cases results in a unique spectrum over the 833 to 1250 cm-1 spectral region. As the initial case 

was generated using ambient temperature (at the same altitude), it can be considered the null case, 

and is subsequently subtracted from the other 99 cases. For a wake vortex core to wall 

temperature difference of 2 K, the resulting radiances can be converted to brightness 

temperatures. The null (no wake vortex) brightness temperatures are subtracted resulting in the 

values shown in Figure 28 below. 

Figure 28. The maximum brightness temperature difference expected to be observed 

by the Hyper-Cam. 

As can be seen, this simulated maximum brightness temperature signal is well below the noise 

level of the Hyper-Cam instrument (the signal is one order of magnitude less than that which is 

observable, see Appendix A). The IR long wave window region has a fairly flat response and < 

0.1 K brightness temperature variation can be expected for a 2 K core to wall wake vortex 

temperature gradient, the maximum response being observed at the spectral location of the water 

vapor line centers. Thus, the wake vortex signal is difficult to see within the spectra range of the 

Hyper-Cam instrument unless the temperature variation is also associated with a variation of the 

water vapor mixing ratio, which was held constant for this simulation. 

As a check on this simulation, Figure 29 shows the sensitivity of the ground-based Hyper-Cam 

spectral measurements to a 1 K temperature variation within a 1-meter path length as a function 

of Hyper-Cam distance from the vortex. 
61 

Figure 29: The sensitivity of ground-based Hyper-Cam spectral radiances to atmospheric 

temperature variations of 1 K within a 1-meter layer, as a function of distance from the instrument. 

Each curve shown represents a different spectral channel. 

The results shown in Figure 29 are in good quantitative agreement with those shown in Figure 28. 

It is also shown that if the Hyper-Cam spectral response could be extended to obtain CO2 channel 

measurements within the 700 – 800 cm-1 spectral region, the sensitivity would be doubled. 

However, the results presented in Figure 28 and Figure 29 indicate that it is very difficult to see 

wake vortices with the Hyper-Cam if the radiance signal is produced solely by temperature 

variations. It is possible that water vapor variations produced by the wake vortex may produce 

radiance signal variations much larger than those shown here for temperature alone. The impact 

of water vapor, and other trace gases associated with the wake vortex, needs to be investigated 

before any conclusions can be made regarding the Hyper-Cam’s ability to sense wake vortices. 

TASS model data for the A319 aircraft at 1-km altitude was used for simulation of the wake 

vortices as input to the FLI model. For these test cases, the relative humidity was varied, 

producing water vapor mixing ratio variations for a constant temperature (i.e. the US Standard 

Atmosphere). Relative humidity is based on a saturation water vapor mixing ratio (computed at 1-

km altitude and temperature 281K) of about 3 g/Kg. The separation distance from the aircraft to 

the wake vortex for the single pixel study was 1 km. Results of the simulations are shown in 

Figure 30. 
62 

Figure 30: TASS wake vortex radiance simulations for different relative humidity conditions. 

The water vapor radiance variability is significantly larger than the expected nominal noise of a 

FLI system (i.e. NEdT = 0.1 K). Also, an imaging FTS should provide a spatially coherent image 

of vortices (in this head-on configuration) which is not represented by this single pixel 

parameterization. The next step is to consider multiple pixels to simulate the image that would be 

provided by a FLI instrument. 

Taumi Daniels completed a PhD thesis on the detection of wake turbulence using infrared 

radiance measurements in March 2012 [23]. The following models, through Figure 33, are 

highlights from this thesis. Simulations of the atmospheric temperature, moisture, and exhaust gas 

conditions associated with wake vortices from commercial jet aircraft were simulated using 

TASS. LBLRTM was used to simulate measurements of the infrared radiance spectrum (650 – 

3350 cm-1 @ 0.5 cm-1 spectral resolution) that would be measured with a spectrometer when 

viewing the atmospheric disturbance created by the wake of a Boeing 747 jet aircraft. As can be 

seen in Figure 31, there is a small, but evident, temperature and moisture structure signature of 

the wake vortex disturbance. 
63 

Figure 31. The temperature (left panel) and moisture (right panel) distribution associated with the 

wake of a Boeing 747 jet aircraft. 

Computations for the standard deviation of the spectral radiance disturbance produced by the 

wake vortex measured by a ground based spectrometer viewing the wake are shown in Figure 32. 

The natural elevation angle dependence of the radiance emission has been removed to more 

clearly isolate the wake vortex signal. 

Figure 32. The standard deviation of the radiance observed across the field of regard of an imaging 

spectrometer associated with the temperature and moisture disturbances shown in Figure 31. 

As shown, the wake vortex causes the radiance to vary across the instrument’s field of regard 

with a standard deviation as high as 0.02 K, dependent upon the spectral frequency being 

observed. The CO2 radiance signatures are due to the wake vortex atmospheric temperature 

disturbance, whereas the water vapor radiance signatures are due to the combined effects of the 

temperature and water vapor mixing ratio disturbances shown in Figure 31. Furthermore, it is 

shown that the wake vortex radiance signatures occur over broad spectral bands of carbon dioxide 

and water vapor emission, which is important for optimizing the signal-to-noise of a spectral 

radiometer designed for detecting wake vortex turbulence. 
64 

The measurements of the radiance associated with wake vortices generated by commercial jet 

aircraft were conducted both at Madison, Wisconsin (June, 2008) and Atlanta, Georgia (July 

through September 2010) using the Telops Mid-wave Hyper-Cam and Long-wave Hyper-Cam 

instruments, respectively. Extensive analyses of the data collected did not reveal any detectable 

radiance signal of the wake vortices being observed. Note, however, that winds in Madison were 

extremely high and likely prevented vortices from forming, and that the Atlanta data acquisition 

activity was meant as a preparation activity for a formal data acquisition activity to understand 

requirements and settings. 

Figure 33 illustrates one reason for this lack of success from these experimental measurements. 

The spectral bands of the Telops Long-wave and Mid-wave Hyper-Cam instruments are shown in 

the top diagram. The optimal band pass for an imaging radiometer designed to detect wake 

turbulence is shown in the lower diagram of this figure. As can be seen, both the Long-wave and 

Mid-wave Hyper-Cam instruments operate across spectral bands where the wake vortex radiance 

signal is a minimum. Furthermore, the noise level of the Hyper-Cam measurements is more than 

two orders of magnitude larger than the wake vortex signal for the spectral bands observed (see 

Appendix A). Note that data were collected in Madison with a broadband spectrometer that 

covered the optimal band pass, but again, the winds were so high that vortices likely did not form 

or were blown out of the field of view before forming. Also, these tests were ground based, and 

the optimal band pass shown on the bottom plot of Figure 33 are for flight altitudes. The optimal 

band pass for a slant path from ground level will be described at the end of this section. 

Figure 33. The standard deviation of the radiance spectrum (natural elevation angle dependence 

removed) as would be observed with a spectral resolution imaging spectrometer operating at a 

spectral resolution of 16 cm-1 at two different aircraft altitudes, 0 km and 6 km. 
65 

Figure 33 demonstrates that it should be possible to detect wake vortex turbulence, and 

presumably mountain lee wave and clear air turbulence if an appropriate sensor may be 

identified. Instrument requirements based upon the above models will be defined in Section 11. 

Wake vortex radiance simulations were also conducted with FLI-FM2 to estimate the magnitude 

of the signal from a broadband instrument sensitive in the shortwave through long-wave spectral 

regions. The temperature and water vapor 3-D fields used in the simulations were prepared with 

TASS at NASA Langley Research Center (the same TASS model was used for the LBLRTM 

simulations above). The full TASS domain size is 800 m by 600 m by 600 m in the along-track 

(X), cross-track (Y), and vertical directions (Z), respectively. The spatial grid resolution was 1.5 

m in the cross-track Y-Z plane and 2.0 m in the along-track direction, resulting in 404 x 404 x 

404 pixels. Temporal development of the wake vortex was represented by seven data cubes, 

starting from t = 0 s and ending at t = 60 s, with a 10 s time increment. 

The observer point was located on the ground level, at a distance about 700 m from the front 

cross-track Y-Z plane of data. The viewing elevation angle,  for the center image pixel was 

equal to 30°. The detector matrix was assumed to have 25 x 25 pixels in the vertical and 

horizontal directions, with a full angular field of view 12° x 12° (± 6° from the center pixel), i.e. 

the pixel angular size is 0.5° (~10 mrad). 

The instrument FOV, being projected to the TASS front cross-track plane, results in observation 

of an area that has a 300–500 meter altitude range with ± 84 m span in the horizontal direction, 

which corresponds to a simulated wake vortex location in the center of the image. 

The referenced atmosphere was built from the mean temperature and water vapor vertical 

profiles, T(H) and Q(H), respectively, obtained by averaging over the horizontal X-Y plane plus 

averaging over all seven time moments. These mean vertical profiles are shown in Figure 34. 

Figure 34. Total mean temperature and water vapor vertical profiles from TASS data. 

Temperature, K

298 299 300 301 302 303 304

Altitude, m

0

100

200

300

400

500

600

H2O, g/kg

5678

Altitude, m

0

100

200

300

400

500

600
66 

The temporal variability of the horizontally averaged profiles caused by the wake vortex is 

presented in Figure 35 and Figure 36 for T(H) and Q(H) respectively. Because deviations of each 

cube mean profile from the total mean are comparatively small, the left panels in Figure 35 and 

Figure 36 show their difference, while the right panels present one-sigma profiles for each 

altitude level. 

Figure 35. Deviation of the mean temperature vertical profile at the moment t from the total mean 

profile (left panel), and temperature horizontal variability for each altitude level (right panel). 

Figure 36. Deviation of the mean water vapor vertical profile at the moment t from the total mean 

profile (left panel), and water vapor horizontal variability for each altitude level (right panel). 

Mean(t)  Mean(all t)

Temperature, K

-0.03 -0.02 -0.01 0.00 0.01 0.02 0.03 0.04

Altitude, m

0

100

200

300

400

500

600

1 sigma

Temperature, K

0.00 0.02 0.04 0.06 0.08 0.10 0.12

0

100

200

300

400

500

600

t  0 s

t 10 s

t 20 s

t 30 s

t 40 s

t 50 s

t  60 s

Mean(t)  Mean(all t)

H2O, g/kg

-0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04

Altitude, m

0

100

200

300

400

500

600

1 sigma

H2O, g/kg

0.00 0.05 0.10 0.15 0.20 0.25 0.30

0

100

200

300

400

500

600

t  0 s

t 10 s

t 20 s

t 30 s

t 40 s

t 50 s

t  60 s
67 

Because TASS data continues only up to an altitude of 600 m, and the whole atmospheric profile 

is needed to perform radiative transfer calculations, the special TASS Referenced Atmosphere was 

constructed by smooth merging the TASS mean profiles with the US Standard Atmosphere. 

Figure 37 shows how this merging has been performed. For the temperature profile, the TASS 

vertical temperature gradient was kept up to an altitude of 12 km (tropopause), and then the 

standard profile was added. For the water vapor profile, the TASS mean profile was merged with 

the standard profile at the altitude of 2 km by interpolation. 

Figure 37. TASS Referenced Atmosphere (black) obtained by merging actual TASS mean profiles 

(red) with the US Standard Atmosphere (green).

Radiative transfer calculations were then performed with FLI-FM2. The wavenumber range 

2200–2400 cm-1 was chosen for the simulation. Initial high-resolution spectra were computed 

with a 0.25 cm-1 wavenumber increment. Gaussian apodization with an interferometer equivalent 

maxOPD = 2 cm was applied as the FLI-FM2 monochromatic radiance convolution. Then, the 

broadband signal was obtained by simple averaging over the entire wavenumber range. Spectra 

were computed for each pixel and each time moment, which resulted in 25 x 25 x 7 = 4375 “wake 

vortex” spectra total. 

In addition, one more cube of 25 x 25 = 625 spectra total was computed for the TASS Referenced 

Atmosphere to present background radiances R0(ν, α, β), where (α, β) are azimuth and elevation 

angle pixel coordinates. A wake vortex signal S was estimated as the radiance difference in both 

high resolution and broadband domains: 

(4.1.1) 

It should be noted that path RT-integration through the TASS data cube was done with a TASS 

vertical resolution increment to account for the wake vortex signal with as high accuracy as 

possible. Background radiances were computed the same way; the only difference is that TASS 

data were replaced with the TASS Referenced Atmosphere for part of the path going through the 

Temperature, K

200 220 240 260 280 300

Altitude, km

0

2

4

6

8

10

12

14

H2O, vmr

0.001 0.01 0.1

Altitude, km

0

1

2

3

TASS Ref.Atm

Real TASS Data

The US Std.Atm

TASS Ref.Atm

Real TASS Data

The US Std.Atm
68 

TASS cube location. Therefore, elevation/azimuth/path radiance dependence was accounted for 

with absolute accuracy. 

Figure 38 shows the brightness temperature spectrum for the central pixel (α = 0°, β = 30°) and 

TASS Referenced Atmosphere, with 0.25 cm-1 spectral resolution. 

Figure 38. Brightness temperature spectrum for the central pixel (α = 0°, β = 30°), TASS Referenced 

Atmosphere. 

Figure 39. Radiance dependence on elevation angle R(β) – R(β = 30°), TASS Referenced Atmosphere, 

brightness temperature units. 

Figure 39 shows the radiance dependence on elevation angle, which can reach ~2 K in this 

spectral region and for the given observation geometry. Radiance dependence on azimuth angle 

has been found to be negligible under the same measurement conditions. 

Figure 40 shows the high-resolution spectral dependence of the wake vortex signal, i.e. the 

statistics of the difference between perturbed and background radiances, obtained by averaging 

wavenumber, cm-1

2200 2250 2300 2350 2400

TB, K

275

280

285

290

295

300

305

310

wavenumber, cm-1

2200 2250 2300 2350 2400



TB, K

-3

-2

-1

0

1

2

3

Elev o

Elev o

Elev o
69 

over all 25 x 25 x 7 radiances. The signal is very weak (less than 0.01 K), while the measured 

value is about 300 K (see Figure 38). 

Figure 40. Statistics (mean and 1 σ) of the difference between wake vortex perturbed and 

background radiances, obtained by averaging over all pixels and time moments, brightness 

temperature units. 

Temporal development of the wake vortex is presented in Figure 41. It shows the broadband (i.e. 

averaged over 2200 – 2400 cm-1) difference between perturbed and background radiances for the 

whole field of view and all time moments separately. This figure demonstrates that the wake 

vortex signal can reach 0.01 K one minute after its generation. 

Figure 41. Evolution and magnitude of wake vortex signal as it would be seen by broadband 

instrument in the 2200 – 2400 cm-1 spectral region. 

wavenumber, cm-1

2200 2250 2300 2350 2400

TB, K

0

2e-3

4e-3

6e-3

8e-3

1e-2

Mean

1 
70 

Wake vortex radiance simulations using the updated FLI-FM2 were then extended to the LW and 

MW bands. The ultimate goal of these simulations is to find the optimal channels for a broadband 

spectrometer where the wake vortex signal has a maximum value. The same TASS wake vortex 

temperature and water vapor 3-D fields were used. The geometry of the observations was 

changed slightly in order to get most of the wake vortex signal into the field of view. Figure 42 

compares the field of view used in the SW-simulations with the new one. The expected wake 

vortex signal for a broadband spectrometer with 50 cm-1 width channels is shown in Figure 43. 

Two more bands located at about 700 cm-1 and 1250 cm-1 were found where the wake vortex 

signal is a maximum. These bands are in addition to the 2000 – 2200 cm-1 SW region explored 

earlier. Figure 44 shows images of a wake vortex, one minute after its generation and at the three 

optimal bands, respectively. 

Figure 42. Updated FOV for MW and LW wake vortex simulations, represented by red box. 

Figure 43. Brightness temperature RMS difference between wake vortex perturbed and referenced 

atmospheric state for a broadband spectrometer with 50 cm-1 width channels. 
71 

Figure 44. Wake vortex signal at LW, MW, and SW optimal bands. 

The reader may note that the optimal band passes for detection of wake vortices identified by the 

LBLRTM and FLI-FM2 simulations do not seem agree. The LBLRTM simulation shown in 

Figure 33 indicates an optimal band pass of 1300 – 2000 cm-1, and the FLI-FM2 simulation 

shown in Figure 43 indicates three optimal band passes of 700 cm-1, 1250 cm-1, and 2000 – 2200 

cm-1. However, there are two plots in Figure 33; the top plot shows the LBLRTM simulation at 0 

km elevation, and the bottom plot shows the LBLRTM simulation at 6 km elevation. The optimal 

band pass drawn on Figure 33 is for the 6 km elevation. The FLI-FM2 models were performed at 

0 km elevation only. For the 0 km simulations, the observer is on the ground and looking at a 

slant path through layers of atmosphere. For the 6 km simulation, the observer is at 6 km and is 

above these layers, looking horizontally. If one compares only the top plot of Figure 33 with 

Figure 43, they are very much in agreement, except for a peak at ~1600 cm-1, which is probably 

due to differences in FFT-transfer, applied apodization, and broadband averaging between 

LBLRTM and FLI-FM2. 

The conclusion that the optimal band passes are different for a wake vortex signal simulated 

along a slant path from ground versus a horizontal path at flight altitude is an important 

consideration when setting the requirements of a FLI sensor. However, for both elevations, there 

is a peak signal in the range of 1250 – 2000 cm-1. Therefore, if a broadband radiometer with one 

band from 1250 - 2000 cm-1 could be identified, it would be optimal, as it could be used for wake 

vortex detection regardless of observer altitude. However, the noise floor would need to be much 

lower than currently available technology. The Suomi NPP CrIS was sent into space, and its 

detectors have noise levels representative of state of the art in current detector technology. The 

CrIS has a noise level of 0.06K, or 60 mK, which is still not low enough to detect the wake vortex 

signal. The noise level of this sensor must also be considered; the sensor requirements are 

summarized in Section 11. 

4.2 Wake Vortex Measurements 

In October 2009, GTRI first contacted personnel at Hartsfield-Jackson International Airport 

(ATL) to begin discussions regarding ground-based wake vortex field testing. The discussions 

continued with meetings between GTRI and ATL personnel in November and December 2009. 

The purpose of these tests was to optimize test procedures for attempting to image wake vortices 

with the Hyper-Cam in anticipation of a full-scale data collection activity at San Francisco 
72 

International Airport (SFO). A runway was identified at ATL (27 R, boxed in red on Figure 45) 

as well as a location for our equipment (see starred area on Figure 45). The equipment location is 

outside the ATL fence, on private property owned by the Jacoby Development Corporation. ATL 

personnel facilitated a meeting between GTRI and Jacoby personnel, and they also offered to 

facilitate interactions/paperwork submission with the FAA and with airlines to enable GTRI to 

conduct the tests at this site. 

Figure 45: An aerial view of the runway and equipment location for wake vortex testing. 

Jacoby granted GTRI permission to use their land for testing. A site visit was held on March 4, 

2010 with GTRI, ATL (Rob Rau), and Jacoby (Todd Addison) personnel in attendance. The site 

is approximately 25 minutes driving time south of GTRI, allowing for easy set up and tear down, 

as necessitated by weather or other factors. It was determined that no paperwork needed to be 

filed with the FAA for us to use this site, based on permissions that Jacoby already had in place 

for their development of the site. GTRI was allowed to have a ~10x10 tent to keep direct sun off 

of the instruments. 

The first data collection activity at ATL was completed July 23, 2010. The Hyper-Cam was set up 

at the end of a runway, facing the direction of oncoming landing aircraft. The Hyper-Cam was 

oriented facing east, so as incoming aircraft landed, they passed over the test site and through the 

field of view. 

The Hyper-Cam has an IFOV of 0.35 mrad and a variable window size of up to 320 x 256 pixels. 

For the July field test, reduced window sizes were used in order to decrease the recording time. 

The resolution of the Hyper-Cam was varied; sets of measurements were recorded at 1, 4, and 

16 cm-1 resolutions. The Hyper-Cam was aimed eastward toward the landing aircraft; the setup is 

illustrated in Figure 46. The Hyper-Cam was inclined upwards to capture the flights, and the 

elevation angles, along with other instrument settings, are given in Table 6. 
73 

Figure 46. The test setup just off runway 26R at Hartsfield-Jackson International Airport in Atlanta, 

GA. 

Table 6. Hyper-Cam instrument settings for July 23, 2010 ATL field test. 

Acquisition 

Run Number 

Start Time 

(EDT) 

Elevation Angle 

(degrees) 

Resolution 

(cm-1) 

Window 

Size 

1 8:25 AM 10 4 200 x 150 

2 8:29 10 4 200 x 150 

3 8:34 10 4 150 x 75 

4 8:35 10 4 150 x 75 

5 8:38 10 4 150 x 75 

6 8:39 10 4 150 x 75 

7 8:43 10 1 200 x 75 

8 8:45 10 1 200 x 75 

9 8:46 10 1 200 x 75 

10 9:04 10 16 200 x 75 

11 9:05 10 16 200 x 75 

12 9:07 19 16 200 x 75 

13 9:18 19 4 200 x 75 

The second field test at Hartsfield-Jackson International Airport was conducted on August 27, 

2010. However, on this day, because of local wind conditions, the aircraft were not landing in the 

same pattern as shown in Figure 46. Instead of landing from the East over the test site, the aircraft 

were taking off from the West, slightly south of the test site. Therefore, instead of looking 

directly at oncoming aircraft, the hyperspectral datacubes were collected viewing aircraft from 

the side at an angle; this difference in viewing angle is shown in Figure 47. Twenty-two 

measurement runs were made over the time in which over forty takeoffs occurred. 
74 

Figure 47. The locations of the Hyper-Cam in July and in August. The red arrows indicate the 

Hyper-Cam’s viewing direction. 

The third and final field test was conducted on September 22, 2010. The test site setup was 

identical to the data collection activity conducted on July 23, 2010, shown in Figure 46. A photo 

from this field test is shown in Figure 48. 

Figure 48. September 2010 ATL field test setup with Hyper-Cam. 

Data from several flights were collected, as well as data looking straight up to obtain a zenith sky 

measurement. For this field test, 16 cm-1 resolution datacubes were collected with a window size 

of 320 x 256 (the full FOV). 

Lidar and METAR data were also collected at the Georgia Tech Campus in conjunction with the 

ATL measurements. The July 23 data run started off with a raw METAR at 11:53 AM GMT of 

METAR KFTY 231153Z 00000KT 6SM HZ CLR 27/23 A3013 RMK AO2 SLP190 70001 

T02670233 10278 20250 53017, which shows that winds were calm (00000KT), there was a bit 

of haze (HZ), but the skies were clear of clouds (CLR). The METAR that was recorded the 
75 

closest to the end of the run, at 1:53 PM GMT, METAR KFTY 231353Z 02003KT 10SM CLR 

31/24 A3014 RMK AO2 SLP194 T03060239 shows that winds (02003KT) had picked up a little 

with a speed of 3 knots (03) in the north-northeasterly direction (020 signifying 20 degrees which 

is labeled as being in the NNE direction), the skies remained clear (CLR) until the end of the run 

and the haze (HZ) disappeared. 

The July 23 Range-Corrected Backscatter plot in Figure 49 shows clear skies, free of cirrus 

clouds, up to 8km. There is a small amount of returns (approximately 14 x 10-4), corresponding to 

trace amounts of aerosols, from 0-1 km and then even fewer returns from 1-4 km. 

Figure 49: Range Corrected Backscatter plot for the UV Lidar, July 23, 2010. 

4.3 Wake Vortex Analysis 

The first detection mechanism investigated for wake vortices was brightness temperature 

differences caused by the temperature structure of the vortices. Previous reports had suggested 

that such contrasts would be measureable in the LWIR spectral region [16]; however, simulations 

described in Section 4.1 of this report show that the signal is too low to be detected by the HyperCam. It is possible that water vapor variations produced by the wake vortex may produce 

radiance signal variations much larger than those shown here for temperature alone. The impact 

of water vapor and other trace gases associated with the wake vortex needed to be investigated 

before any conclusions can be made regarding the Hyper-Cam’s ability to sense wake vortices. 

For these reasons, a detection mechanism based on entrained exhaust gasses was attempted. 

Much of the following section is from an SPIE paper by FLI team members [20], which is 

included as an appendix to this report. 

4.3.1 Detection Algorithms for Entrained Exhaust Gases 

As wake vortices form and then descend, there is no a priori way to tell where in the field of view 

they exist. They could form below the captured scene or be carried away by crosswinds and not 

be captured at all. With no way to determine where in the scene the vortices are located, a search 

methodology was developed which exploits the fact that vortices tend to entrain exhaust gases, 

such as CO2, CO, NOx, and H2O [15]. The spectra of these gases, if they exist in the datacubes, 
76 

are in mixed pixels that may show spectral influences from all scene elements, including the gas 

of interest, the background, and the intervening atmosphere. Because these gases are in the 

exhaust, if they can be detected immediately upon exiting the aircraft engine, it could be possible 

to follow the gases through the next datacubes in the sequence of measurements as they either 

become entrained in a vortex or disperse into the atmosphere. 

Gas detection algorithms investigated in this program included the Spectral Angle Mapper (SAM) 

and the Clutter Matched Filter (CMF) [25]; the Matched (MF) and Adaptive Matched Filter 

(AMF), and the Adaptive Coherence/Cosine Estimator (ACE) [26], [27]. All of the algorithms 

depend on finding a correlation or covariance in the data. The SAM algorithm finds the 

correlation between the target gas spectrum and every pixel in the datacube; a SAM result near 1 

strongly indicates that a gas is present. All other algorithms depend on the covariance of the data, 

e.g. how much each pixel varies spectrally with the others. They are variants of the Generalized 

Likelihood Ratio Test [26] [27]. The Matched Filter, the Adaptive Matched Filter, and the 

Adaptive Coherence/Cosine Estimator were chosen as the most appropriate for the entrained 

gasses. 

These gas detection algorithms depend on a priori knowledge of the gas spectrum under search, 

i.e. the target gas spectrum. Under a controlled gas release, gas constituents, abundances, 

temperatures, and concentrations are known. However, in the data sets under investigation, these 

characteristics must be estimated. To model the target gas spectrum effectively, the background 

and the atmosphere must also be considered. Therefore, the online tool Spectral Calc [28], which 

accesses the HITRAN database, was utilized to model the target gas. Many methods of modeling 

the target gas were investigated, including: 

1. Creating the gas spectrum with the Spectral Calc atmospheric path radiance tool, then 

downsampling this spectrum to match the resolution needed. This spectrum then becomes 

the target gas. 

2. Creating the gas spectrum with the Spectral Calc gas cell radiance tool, then 

downsampling this spectrum to match the resolution needed. This spectrum then becomes 

the target gas. 

3. Creating the gas spectrum with the Spectral Calc gas cell radiance tool, smoothing it with 

a Lorentzian or Gaussian function with a width equal to the resolution needed, then 

downsampling this spectrum to match the resolution needed. This spectrum then becomes 

the target gas. 

4. Creating the gas spectrum with the Spectral Calc gas cell radiance tool, using Spectral 

Calc’s built in Gaussian smoother to smooth it, then downsampling this spectrum to 

match the resolution needed. This spectrum becomes then becomes the target gas. 

5. Creating the gas spectrum in the same manner as 2 through 4, only the final spectrum is 

added to an average of the background from the actual datacube. Each horizontal strip of 

sky is averaged independently. 

The gases searched for were H2O, CO2, NO2, and HNO3. As the largest component of the 

exhaust, water vapor at 425 K was chosen as the first target. The temperature was based on a 

derivation of aircraft exhaust temperature following the method described by Mattingly [29]. 

The best results were obtained when the gas cell spectrum was smoothed with the Spectral Calc 

Gaussian smoother, and the result added to the background averages. This process is thus 
77 

described in more detail here. The gas cell radiance spectrum was downloaded from Spectral Calc 

after having been smoothed with the Gaussian and the resulting spectrum was interpolated to 

match the resolution needed for each datacube that was collected. This result was combined with 

the measured data in the following manner. First, the datacube following the passage of the 

aircraft was divided into horizontal strips of sky and the radiance of each strip was averaged. 

These averages were done for each piece of sky independently, because the radiance of the sky is 

expected to change with elevation angle. This method of averaging the radiance for horizontal 

strips of sky is similar to the Directional Mean Filter [30], with the exception that we limited the 

direction of our background average to the horizontal. The resulting radiances give a measure of 

the background atmospheric conditions as a function of elevation angle at the time of 

measurement. Next, the gas cell data was added to each resulting radiance average; the gas cell 

data was added because in this case, the hot engine exhaust was expected to act as an emitter 

against the cold sky. This method gives the semi-empirically modeled target gas spectrum for 

each elevation angle. 

A median filter was used on these images to eliminate noise. The result of the median filter was 

an image where every pixel takes the value of the median value of the 3 x 3 block of pixels 

around it in the original image. This filtering serves to eliminate pixels that score positively but 

have no positive scoring pixels around them, and are more likely false detections. 

Originally, the CMF and the SAM algorithms were going to be run on the data from the 

September field test at ATL. These data were selected because the Hyper-Cam was allowed to 

run continuously for every flight instead of being started and stopped before and after each 

aircraft landing. However, this entire data set was collected at 16 cm-1 resolution. Also, only the 

data collected at zero zenith angle had valid calibration files; the sets angled to catch the crosssection of any falling vortices had blackbody files that were somehow corrupted. Therefore, the 

July dataset was analyzed more extensively. The July dataset had seven flights collected at 4 cm-1 

resolution. The drawback with this set is that the Hyper-Cam did not run continuously, so the 

recording of vortex formation may have been cut off by the end of the measurement. 

Two datacubes from the July dataset showed potential detection of exhaust plumes in the sense 

that the high-scoring pixels from the algorithm tests were located in an area of the image 

consistent with being part of an exhaust plume. One of the datasets was recorded at 4 cm-1

resolution, and results from its analysis are shown here. Water vapor was searched for in the cube 

following the passage of the aircraft and in all other succeeding cubes until that run ended. The 

visible image and a radiance image of the aircraft itself are displayed in Figure 50. 

Figure 50. The visible image of the aircraft that passed through the FOV (left), and the radiance 

image of the same aircraft (right).
78 

The three algorithms that were run on the cube following the passage of the aircraft (cube 1452) 

were the MF, AMF, and ACE. Water vapor was the target spectrum. The radiance image of the 

entire datacube is shown in Figure 51, and the results of the algorithms are shown in Figure 52 

through Figure 54. 

Figure 51. Average radiance of entire datacube 1452. 

Figure 52. The MF results on datacube 1452. 

Figure 53. The AMF results on datacube 1452. 

Figure 54. The ACE results on datacube 1452. 

The target in all three runs is water vapor, and two trails in the air behind the aircraft are 

definitely detected; however, whether or not the algorithms are detecting water or anomalies is 

debatable. Nevertheless, there are two distinct trails, on the right side of all three images above 

that are not present in the radiance image of Figure 51. 

All the algorithms depend on some kind of comparison of the target spectrum with the 

“demeaned data.” This demeaned data is the spectrum of each pixel with the background 
79 

subtracted out. The average background is computed for every row of sky, since the sky radiance 

changes greatly with increasing viewing angle. Once the vapor trails were located, the algorithms 

were re-run, but when computing the average background for the demeaned data, only columns 1 

– 90 (left side) were used so that we ensured no plume was present in our background average. 

The results of these tests are shown in Figure 55 through Figure 57. 

Figure 55. The MF results on datacube 1452, using only columns 1 – 90 as background in the 

calculation. 

Figure 56. The AMF results on datacube 1452, using only columns 1 – 90 as background in the 

calculation. 

Figure 57. The ACE results on datacube 1452, using only columns 1 – 90 as background in the 

calculation. 

Comparison of Figure 52 through Figure 54 to Figure 55 through Figure 57 shows improvement 

in the performance of all detectors when the plume-free background is used as an average. 

However, one still cannot say definitively whether water vapor is being detected. Therefore, we 

compared the spectrum of a pixel that scored high for all three algorithms to a pixel that was 

definitely only background. While there were differences in the observed spectra, they were at 

locations that could not be correlated with the water vapor spectrum. 

The evolution of the water vapor trails can be studied by analyzing successive datacubes. The 

results shown in Figure 58 though Figure 63 are for water vapor, searched for with an AMF, to 

which a median filter has been applied. 
80 

Figure 58. The radiance image of the datacube following the passage of the aircraft. 

Figure 59. The adaptive matched filter results 1 second after the passage of the aircraft. 

Figure 60. The adaptive matched filter results 5 seconds after the passage of the aircraft. 

Figure 61. The adaptive matched filter results 10 seconds after the passage of the aircraft. 
81 

Figure 62. The adaptive matched filter results 15 seconds after the passage of the aircraft. 

Figure 63. The adaptive matched filter results 20 seconds after the passage of the aircraft. 

Figure 58 though Figure 63 demonstrate that the water vapor trails dissipate quickly, probably 

because the air behind the aircraft is very turbulent. However, the vapor can still be detected for a 

few seconds on the right side of the image, where the trails once were. We also analyzed a 

datacube before the aircraft entered the field of view, and the right side of that image scores 

higher as well. It may be that, due to the high volume of air traffic at ATL (there was a landing 

approximately every two minutes), the background has enhanced water vapor due to the exhaust 

from a nearly constant stream of incoming aircraft, making detection more difficult. 

Questions arose as to whether the detection algorithms were acting as gas detectors or anomaly 

detectors. Therefore, a more in-depth look at the algorithms themselves was undertaken. Tests 

were run in which random targets were used instead of the actual gases, in an attempt to see 

whether or not the plumes could be detected. These random targets were modeled as vectors of 

zeros with ones inserted at random wavenumbers. In the instance of the MF, the random target 

was found in the area of the plume, which would lead one to believe that it is detecting an 

anomaly and not necessarily the gas itself. Something is being detected in the aircraft exhaust, but 

whether or not this can be identified as a specific gas is not clear. Thus, a methodology was 

developed to see if, once detected, the anomaly could be identified in a second step by 

comparison of its spectrum to that of a background pixel. 

Gas detection algorithms run on ATL data using a plume-free background as an average showed 

improvement in performance; however, water vapor could still not be identified. In order to 

attempt to identify water vapor in the plume, the pixels in a 6-pixel high by 4-pixel wide region of 

plume that scored highly for all detection algorithms were averaged and labeled as plume. Then, 

the same 6-pixel high strip of sky was taken from a datacube that was recorded two frames before 

the airplane entered the field of view. These pixels, once averaged, were labeled as background. 

The plume and background plots are shown in Figure 64. 
82 

Figure 64. The radiance of the averaged plume pixels, plotted with the radiance of the averaged 

background pixels. 

No obvious differences between the two spectra can be seen in Figure 64; therefore, to see if any 

differences do exist and where, the background was subtracted from the plume and the residuals 

plotted in Figure 65. 

Figure 65. The difference of the plume and background. 

The differences seen in Figure 65 are small and are not correlated with spectral features of the 

water vapor spectrum. It has not been possible to identify particular gases by the methods 

described here. 

4.3.2 Principal Component Analysis 

In addition to the methodology described above, Principal Component Analysis (PCA) on the 

data cubes from July 23, 2010 was also performed. After applying the bad pixel correction, these 
83 

difference images were examined visually for any potential wake signatures. Figure 66 through 

Figure 68 are sample images from the PCA reconstruction or filtering technique are shown below 

for each of the three resolutions collected on that day. This method revealed no detection of wake 

vortex signatures. 

Figure 66. PCA filtered difference image integrated over 980 to 1100 wavenumber range, calculated 

for a 1 cm-1 resolution datacube. 

Figure 67. PCA filtered difference image integrated over 1100 to 1350 wavenumber range, calculated 

for a 4 cm-1 resolution datacube. 

Figure 68. PCA filtered difference image integrated over 1100 to 1350 wavenumber range, calculated 

for a 16 cm-1 resolution datacube. 

4.3.3 Wake Vortex Data Analysis Conclusions 

While PCA and several other common methodologies were unsuccessful at identifying wake 

vortices, the investigations described here led to development of a novel methodology for the 
84 

detection of aircraft exhaust trails in hyperspectral data, as well as other mixed pixel gas data that 

has the sky as a background. The best method found is as follows: 

1. Create the target gas spectrum with the Spectral Calc gas cell radiance tool, using 

Spectral Calc’s built-in Gaussian smoother to smooth it. 

2. Downsample this spectrum to match the resolution needed for the Telops datacube under 

study. 

3. Calculate the average radiance of each strip of sky. 

4. Add the target gas spectrum to the average sky radiance to create a semi-empirical target 

model. 

5. Run the MF, AMF, or ACE algorithm, searching for the semi-empirical target gas model. 

6. Display the result as a scaled image. 

7. Eliminate any part of the cube that shows possible detection in the image. 

8. Re-calculate the background average calculated in step 3, but do not include the parts of 

the image eliminated in step 7 in the average. 

9. Add the target gas spectrum to the new plume-free average sky radiance to create a semiempirical target model. 

10. Re-run the MF, AMF, or ACE algorithm, searching for the semi-empirical target gas 

model. 

11. Display the result as a scaled image. 

12. Repeat this process for as many datacubes in the time series that show positive indication 

of detection in the event the gases become entrained in a vortex. 

From the results shown, it is inconclusive as to whether the exhaust gases are actually being 

detected or some other disturbance is being seen. Additionally, if the exhaust is indeed being 

detected, the results indicate that the detection is short-lived, because the same results cannot be 

repeated in successive datacubes of this dataset. The method presented demonstrates a novel, and 

apparently effective, method to search for mixed pixel targets when the sky is the background, 

but we are constrained by the small radiometric signals of the trails. The method developed for 

mixed pixel target detection in a sky background was summarized in a SPIE proceedings paper 

[20], which is also given in an appendix of this report. Although truth data were not available, 

upward-looking hyperspectral imaging in the 800 to 1200 cm-1 region does not appear to be a 

robust means of detecting wake vortices. 

5 Mountain Wave Turbulence 

In early 2011, a Shift in Focus occurred due to changes in priority away from wake vortices and 

terminal area hazards. This shift moved the focus of the project from what was wake vortices to 

mountain wave turbulence, and moved the data collection activity from SFO to the 

CU-Boulder Mountain Research Station (MRS) in Nederland, CO. MRS experiences mountain 

waves and mountain wave turbulence in the fall and winter months, which affect flights into 

Denver International Airport (DEN), therefore it is an ideal location for collecting data on 

mountain waves and turbulence. The mountain wave turbulence data collection and analysis 

comprised the majority of Year 3’s efforts. 

The two main objectives of this test were as follows: 

1. Observe slowly varying radiance patterns associated with standing waves. 

2. Observe short-term fluctuations associated with turbulence. 
85 

These objectives required that long data sets (approximately thirty minutes) at as high as possible 

temporal resolution be collected of the sky by interferometric means. This collection activity 

utilized both the Telops Hyper-Cam and a Designs and Prototypes (D&P) TurboFT spectrometer, 

both operating in continuous mode simultaneously for approximately thirty minutes for each data 

collection event. Each sensor provides unique information about the sky; the Hyper-Cam collects 

LWIR hyperspectral image data of the scene, while the TurboFT has the capability to collect 

approximately 100 spectra per second in the 216 micron region. Both sensors have on-board 

blackbodies, which allow for calibrated radiance in the field. The two sensors were co-located on 

a custom-built mount which allowed boresighting of the sensors for the test so that they would be 

observing the same area of sky throughout each measurement period. 

The Hyper-Cam data addressed the first objective in that the long data sets could capture slowly 

varying radiance, while the TurboFT data addressed the second objective in that the high 

temporal resolution and broader spectrum can capture short-term fluctuations. 

5.1 Preliminary Tests and Data Analysis 

5.1.1 Background 

In a previous phase of this program, FLI simulations were conducted using three-dimensional 

temperature and water vapor fields associated with clear air turbulence. Techniques for 

determining the distance and warning time to an aircraft encounter with clear air turbulence were 

defined. The spectral resolution, range, and instrument noise requirements were defined for a FLI 

to be used to detect CAT and to determine its distance from the aircraft (i.e., warning time) by 

both the temperature and water vapor radiance signal techniques. The information in this section 

(5.1.1 only) is repeated from [15] except for the MODTRAN run at the end of this section to 

provide the reader with the relevant background. 

A high spectral resolution spherical shell radiative transfer model, based on Line by Line 

Radiative Transfer Model (LBLRTM) molecular optical depths, was developed to study 

phenomena at some distance from the aircraft. A pictorial display of the path seen by a FLI 

aboard an aircraft is shown in Figure 69. 
86 

Figure 69. Path viewed by a forward-looking optical instrument aboard an aircraft. 

The model takes the analytical form: 

0 0

(,, ) ( , , ) [ , ( )]*[ ]

TOA s A R v A h B T s ds

s

 



     (5.1.1) 

where  is wavenumber, A is the atmospheric state consisting of pressure (p), temperature (T), 

absorbing constituents (i.e., H2O, CO2, O3, N2O, CO, CH4, O2, NO, SO2, NO2, NH3, HNO3, 

clouds, and aerosols), ho is the height of the aircraft, and B is the Planck function. The 

transmittance of the atmosphere between the aircraft and any position along the viewed path, s, is 

0

( , , ) exp[ ( , ', )] '

s

  s A s A ds    (5.1.2) 

where  is the optical depth. The coordinate along the viewed path, s, is 

2 2

0 () ( ) ( ) e e sh r h r h    (5.1.3) 

where h is height and re is the radius of the Earth. 

Figure 70 below shows the results of applying this model to the US Standard Atmosphere to 

simulate what an FLI would see in clear air when flying on an aircraft at three different altitudes. 

Figure 70. The spectral radiance which would be observed by an FLI looking through the US 

Standard Atmosphere for aircraft altitudes of 1.5, 4.5, and 9.5 km. A Norton-Beer Weak Apodization 

was applied to interferograms simulated to a maximum optical path difference of 1.0 cm (i.e. an 

unapodized spectral resolution of 0.5 cm-1). 
87 

The spectrum corresponds to a near isothermal spectrum (i.e. a Planck function) for the relatively 

opaque 15-m CO2 band, 650-750 cm-1. A near Planck radiance distribution is also observed for 

the strong water vapor absorption region, beyond a lower wave number limit ranging from 

1250 – 1500 cm-1, dependent upon instrument altitude. The strong emission by Ozone within the 

1000 – 1100 cm-1 region can also be seen in Figure 70, with the amplitude of this emission band 

increasing with altitude as a result of the increase in Ozone concentration and the rapid decrease 

of the water vapor concentration with altitude. The strong absorption/emission by methane and 

nitrous oxide near 1305 cm-1 can also be seen from the fact that the spectral radiance distribution 

approaches a Planck radiance spectrum as the altitude of the sensor decreases. 

Figure 71 shows the relative contribution from the atmosphere (assuming US Standard 

Atmospheric conditions) as a function of distance from the aircraft. Contributions greater than 

100% are a consequence of ringing in the spectrum that remains after the weak apodization 

process that was performed. The higher the aircraft, the greater the contribution from spherical 

shells at greater distances from the aircraft. For the sensing of turbulence from the 15-m CO2 

band, most of the signal (> 60 %) is confined to the first 5-km shell ahead of the aircraft for wave 

numbers smaller than 722 cm-1, decreasing with increasing wave number. In the “window” region 

of the spectrum, 800 – 1200 cm-1, the FLI observes contributions from the atmosphere at 

distances greater than 80 km when flying at high altitude (e.g., 9.5 km). This distance makes it 

useful for the detection of hazardous aerosol (e.g. volcanic ash clouds) contributions, providing 

avoidance warning times in excess of 5 minutes for a fast flying commercial jet aircraft. At 

middle and low atmospheric altitudes (e.g. 4.5 km and 1.5 km), the visibility of clouds and 

aerosol layers with distance decreases, being greatest for a narrow spectral region near 1000 cm-1

and the 1100 – 1150 cm-1 region of the spectrum. In addition, high spectral resolution is needed to 

optimize the FLI instrument sensitivity to contributions of radiance far from the aircraft, which 

results from being able to sense the radiation between the water vapor absorption lines within the 

“window” region of the spectrum. 
88 

Figure 71. Contribution to the total radiance from various spherical shells along the atmospheric 

path observed looking forward from an aircraft at three different altitudes: (a) 1.5 km (~850 mb), 

(b) 4.5 km (~580 mb), and (c) 9.5 km (~280 mb). 

Figure 69, Figure 70, and Figure 71 assumed horizontal paths at different altitudes because during 

the previous phase of the FLI program, flight tests were anticipated. However, due to the high 

cost of flight tests, only ground-based measurements were performed during this phase. The 

radiance measured along a horizontal path (i.e. a flight measurement scenario) will be different 

than the radiance measured along a slant path to space (i.e. a ground-based measurement 

scenario). Therefore, path radiances were predicted using MODTRAN with a Midlatitude Winter 

atmosphere for a ground-based sensor at 2.9 km elevation, which corresponds to the elevation of 

the dining hall at MRS. These radiances were computed at five different elevation angles, from 0° 

(horizontal) to 90° (zenith) in 30° increments, with a 15° elevation angle added because this is 

very close to the elevation angle which was measured at MRS. These radiances are shown in 

Figure 72. 
89 

Figure 72. MODTRAN predicted path radiance for a slant path to space, from 2.9 km elevation, at 

five different elevation angles. 

We cannot directly compare Figure 69 and Figure 72 because the simulations were done with two 

models (LBLRTM vs. MODTRAN) and for two atmospheres (US Standard vs. Midlatitude 

Winter). One can still observe that the predicted radiances in Figure 72 are much lower than those 

shown in Figure 69 (the units in Figure 72 are W/m2

-cm-1-sr, to be consistent with Hyper-Cam 

data reported in remaining sections of this report). Additionally, the radiances for a horizontal 

path are included in Figure 72 (magenta line), and is much higher than all other elevation angles. 

The radiance decreases with increasing altitude and increasing elevation angle due to the 

atmospheric attenuation. This MODTRAN simulation shows that, going into the MRS test, there 

would be challenges due to the limitations of ground-based viewing geometries. This limitation 

was taken into consideration in the preliminary mountain wave simulations which are described 

in Section 5.1.5. Despite the lower radiance signals expected for ground-based slant paths versus 

horizontal paths at flight altitudes, the simulations which follow did show radiance signals that 

should have been observable with the Hyper-Cam. 

5.1.2 D&P Sky Measurements 

During the Phase 2 FLI program, radiometric observation of mountain lee waves (an example of 

orographic turbulence) was demonstrated with the D&P spectrometer in Boulder, CO, with 

fluctuations in several spectral regions that were well above the noise level [16] (see Appendix A 

for plots of D&P noise level). These data were analyzed further during this project in anticipation 

of the MRS field test. For comparison, additional D&P sky data were collected in Atlanta under 

clear sky conditions. 
90 

A data collection activity was conducted on the morning of April 29, 2011, under clear sky 

conditions with the D&P TurboFT Spectrometer. The test was made to mimic two D&P 

measurements from the Boulder Field Test at the NCAR Foothills Laboratory in January 2008, 

during a previous phase of this project. Two sky radiance measurements were made in April 

2011, one at a 5 degree elevation angle and one looking vertically. These measurements, recorded 

from the roof of the Baker Building in Atlanta, GA, were conducted so that the data could be 

evaluated to see if the sky radiance time series behavior differs from the Boulder data. In 

addition, a run was made looking only at a blackbody, to ensure that any fluctuations in the sky 

data are indeed due to the sky radiance and not an artifact of the instrument. Table 7 summarizes 

the instrument settings and other details during the test. 

Table 7. D&P instrument settings for April 29, 2011 sky test. 

 Five Degree Elevation Test Zenith Test Blackbody Test 

Resolution (cm-1) 4 4 4 

Number Coadds for Calibration Files 1,000 1,000 1,000 

Number Coadds for Sample Files 24 24 24 

The instrument temperature set point was changed as the ambient temperature increased 

throughout the morning and into the afternoon; the closer the set point was to ambient, the 

steadier it remained throughout all test runs. This temperature was monitored throughout the tests 

and remained steady for all of them; this was important to consider as any indication of 

instrument drift requires re-calibration. The first sky zenith test was stopped after nine minutes 

when clouds began to develop. Another run was made early in the afternoon, after the clouds had 

dissipated and the sun had moved farther west in the sky. 

Both sky measurements were conducted with a 1.2 degree field of view fore-optic, which was 

what was used in the 2008 tests. The standard optic has a 4.8 degree field of view; this optic was 

only used for the blackbody calibration and the blackbody test, because the blackbody attaches 

directly to this optic only. 

Figure 73 shows the setup for the five degree elevation angle measurements. For these 

measurements, the tripod was simply tilted up slightly. An inclinometer was used to ensure the 5 

degree elevation. 
91 

Figure 73. The D&P test setup for the five degree elevation sky measurements. 

For the sky measurements at the zenith, the tripod was leveled and pointed at a gold mirror that 

was inclined at 45 degrees. A level was used to ensure that the center of the optic and the center 

of the gold mirror were properly aligned. This setup is shown in Figure 74. 

Figure 74. The D&P test setup for the zenith sky measurements. 

As a check to ensure that there was no instrument drift that was causing any of the fluctuations in 

sky radiance observations, the calibration blackbody was measured as a sample. The blackbody 

temperature was set at 24.4°C, which was close to ambient. 

The data obtained from this field test proved to be very noisy due to RFI on the roof of the 

building. Therefore, the test was repeated on July 12, 2011 at the Bradley Observatory on the 

Agnes Scott College campus. This location was expected to have much lower RFI, and the 

observatory has a lidar that was operational during the test to ensure that the sky was cloud-free. 
92 

The D&P settings were identical to those shown in Table 7; however, the 5 degree elevation runs 

were not made due to the appearance of clouds during the test. There were unfortunately similar 

data issues with this test as well and the data were not very useful. The results of this test 

indicated that the D&P was in need of refurbishment; it was sent back to the manufacturer is 

September 2011 in anticipation of the MRS field test. 

5.1.3 D&P Data Analysis of Previously Collected Data 

STAR performed further analysis of sky radiance spectra that were recorded by GTRI personnel 

at the NCAR Foothills Laboratory in a previous phase of the program in January 2008 [16], in 

addition to the two data sets collected in Atlanta in 2011. The spectra were recorded with a D&P 

TurboFT spectroradiometer, which covers the 2 - 15 micron wavelength region (666–5,000 cm-1) 

at 4 cm-1 resolution. Large sets of spectra were acquired, corresponding to long time series of data 

points at each wavenumber, and those time series provided the basis for the analyses presented 

here. 

From Boulder, the data included Jan 14th, 15th, and 17th. The first day was calm, and for the data 

period shown in the plots, the device was pointing vertically. The second and third days were 

windy and mountain waves were present, and there were numerous instances of turbulence as 

measured by UAL 757s flying in and out of DEN. As a control, data were collected at GTRI on 

April 29, 2011. There was significant contamination in those data, so a second data set was 

collected on July 19, 2011. Unfortunately, these data had similar problems. In the following, a 

description of the data analysis methodology is presented, followed by a presentation of the 

analysis results. 

For each of the data collection periods, the D&P sensor measured interferograms at a rate of 

approximately 96 samples/sec. Twenty four interferograms were averaged and then Fourier 

transformed to provide radiances as a function of time and wavelength (or wavenumber), as used 

below. The wavenumbers ranged from 630 to 3960 cm-1. During processing, 5000 samples 

(approximately 1500 seconds, or 25 minutes) were analyzed at a time. This number of samples 

was somewhat arbitrary and was chosen as a balance between a long-enough time series and a set 

of data that could be processed in a reasonable amount of time. It should be noted that some data 

collection windows were shorter than 1500 seconds, so in those cases the data available were 

used. In other cases, more than 1500 seconds of data were collected at a time. In those situations, 

the data were separated into non-overlapping windows of 5000 samples. Henceforth, we refer to 

the 5000 sample-long (or less than 5000 if that was all that was available) data set as the analysis 

window. 

Each window was broken into non-overlapping sub-windows of 512 points each, with the leftover data samples ignored. Over each of these sub-windows, a quadratic function was fitted to the 

data, and then subtracted from the data to give “trend removed” data. Probability distributions of 

the radiances over time were calculated and fitted with a Gaussian distribution. Since there should 

be a relatively large number of independent samples over the window, the Central Limit Theorem 

would predict a Gaussian distribution, especially for the trend-removed data. Correlation 

functions for each wavenumber were computed over the entire window. (The Mathematica 

correlation function was used for this computation.) Power spectra were also calculated for 
93 

specific wavenumbers of interest. The spectra were calculated for each sub-window and then 

frequency-averaged to give the spectrum for the given window. A von Karman turbulence modelplus noise and a quadratic function-plus noise were fitted to the spectra. The quadratic model was 

used to investigate whether there was any Brownian noise (f-2) present in the data. Since the 

asymptotic form of the von Karman model has an f-5/3 slope, Brownian noise with an f-2 slope can 

be confused with a true atmospheric structure. Brownian noise can result from averaging white 

noise. 

5.1.3.1 Trend Removal 

It was important to remove the trend prior to the calculations described above. When looking for 

turbulence signatures, one is typically investigating deviations from short-time/space scale 

phenomena; and hence, larger-scale (typically) non-turbulent phenomena should be excluded. 

Figure 75 and Figure 76 illustrate the results of the trend removal process. This is a more extreme 

case, showing a fairly significant variation in the radiance as a function of time, as seen in Figure 

75. Recall that the sub-window used for trend removal is 512 points long, so even after the trend 

has been removed, some moderate-scale variations can still be seen (Figure 76). Figure 77 and 

Figure 78 show estimates of the probability distributions (histograms) for the original and trend 

removed data, respectively. In those figures, the red curve is a smoothed version of the histogram 

data, and the black curve is a Gaussian distribution with the mean and standard deviation coming 

from the histogram data. The bimodal structure seen in Figure 77 is due to the flattening of the 

radiance data at both ends, as seen in Figure 75. After the trend removal the data looks relatively 

consistent with a zero-mean Gaussian distribution, as seen in Figure 78. 

Figure 75. Radiance time series. 
94 

Figure 76. Same data as shown in Figure 75, after trend removal.

Figure 77. Probability distribution (histogram) for the data shown in Figure 75. The red curve is a 

smoothed version of the underlying histogram, and the black curve is a Gaussian distribution fit to 

the histogram. 

Figure 78. Probability distribution for the data shown in Figure 76, i.e. the trend-removed data. 
95 

The importance of removing a trend is clear from Figure 79, which shows the correlation function 

for the original (black curve) and trend removed (red curve) data. Note that the correlation 

function is normalized so that the value at zero lag is always one, hence the apparent jump in the 

red curve. It can be seen that the trend creates an artificial correlation in the data. 

Figure 79. Correlation functions with and without trend removal. 

Mathematically, this can be seen as follows. The normalized correlation function is given by 

(5.1.1)

where as an approximation, 

(5.1.2)

Assume for simplicity that the mean X ̄ is zero, and that for not-too-large values of i, the data can 

be approximated by a linear function of i, 

(5.1.3)

This approximation means that 

(5.1.4)

where 

(5.1.5)

and 

(5.1.6)
96 

Equation 5.1.1 implies that the correlation function in this case is a linear function of τ. We can 

further analyze this via a simple simulation. From the original radiance time series, a linear fit 

was calculated. We created a time series by adding the linear fit to a Gaussian noise sequence 

with mean and standard deviation calculated from the original time series. These quantities are 

shown in Figure 80, where the black curve is the original data (i.e. as seen in Figure 75), the red 

line is the linear fit to the original data, and the blue curve is the simulated data. Figure 81 shows 

the results of the analysis. The black and red curves are those from Figure 79, whereas the blue 

curve is the correlation function calculated from the simulated data. It can be seen that the 

correlation functions for both the original and simulated data are quite similar, and both are linear 

functions. This provides a good illustration of how trends can produce artificial correlations, and 

hence why it is important to remove such trends. 

Figure 80. Black curve is radiance for wavenumber 961; Red curve is fitted linear trend; Blue curve 

is fitted linear trend with Gaussian noise (mean and std. deviation from radiance data). 

Figure 81. Black and red curves are as seen in Figure 79; blue curve is the correlation function for 

the simulated data (blue curve) from Figure 80. 

Figure 82 and Figure 83 show the spectra calculated from the original and de-trended data, 

respectively. As mentioned above, the spectra were fit to a quadratic function plus noise (blue 

curves) and a von Karman function plus noise. As with the correlation functions, there is a 

dramatic difference at the low frequencies between the spectrum from the original and de-trended 
97 

data. This behavior is expected since removing a trend acts as a high-pass filter. Figure 84 is a 

direct comparison between the original and de-trended spectra. Figure 85 illustrates the original 

and de-trended spectra from a different wavenumber and from a different day. In this case, as can 

be seen in Figure 86 and Figure 87, there is not too much difference between the original and detrended data, i.e. there is not much of a trend. From looking at numerous cases, it appears that 

trends are more prevalent in the lower wavenumber data. 

Figure 82. Power spectrum of radiance time series (black). The blue curve is the fit of a quadratic 

function plus noise to the data, and the red curve illustrates using a von Karman model plus noise. 

Figure 83. Same as Figure 82, but with trend-removed radiance data. 
98 

Figure 84. The effect of trend removal on the power spectra. Wavenumber 961 shown. 

Figure 85. The effect of trend removal on the power spectra. Wavenumber 2354 shown. (Note that 

this is from a different day as the data shown above.) 

Figure 86. Probability plot for non-trend removed data used in Figure 85. 
99 

Figure 87. Probability plot for trend removed data used in Figure 85. 

Another type of trend that was observed is a sinusoidal one, as seen in Figure 88. Figure 89 shows 

the result of the quadratic trend removal process, as described above. Another approach to trend 

removal is using Fourier methods; an example of which is as follows. A discrete cosine transform 

of the data was calculated and all the coefficients past the first 60 (arbitrary number), were set to 

zero. The inverse cosine transform was then applied to this new set of coefficients to generate a 

filtered time series. The results of this calculation are shown in Figure 90, where the red curve is 

the original time series, and the black curve is the Fourier-filtered data. The difference of these 

curves, the Fourier de-trended data, is shown in Figure 91, and a comparison of the two power 

spectra is given in Figure 92. Returning to the quadratic trend removal, Figure 93 illustrates the 

correlation functions for the original and quadratic de-trended data (seen in Figure 88 and Figure 

89, respectively). 

Figure 88. Original radiance data showing larger-scale sinusoidal trend. 
100 

Figure 89. De-trended radiance data showing residual, smaller-scale sinusoidal trend. (Using 

quadratic trend removal, as above.) 

Figure 90. Using Fourier method for trend removal. Red curve is the original data as seen in Figure 

88; black curve is trend from Fourier method. 
101 

Figure 91. De-trended data – difference between red and black curves from Figure 90. 

Figure 92. Spectra from original (black curve) and Fourier de-trended data (red curve). 

Figure 93. Correlation functions before and after quadratic trend removal, for the data shown in 

Figure 88 (original) and Figure 89 (de-trended). 
102 

As with the linear trend case, a closed-form correlation function can be computed for a sinusoidal 

trend. We use Equation 5.1.2 with 

(5.1.7) 

and 

(5.1.8) 

where T is the period of the sinusoidal trend. Using , we 

have 

(5.1.9)

and 

(5.1.10)

The correlation function, given by Equation 5.1.1, can then be calculated. The parameters a and T

were estimated by eye from the data shown in Figure 88 as 0.13 and 1800, respectively. Figure 94 

gives the calculated correlation function. A comparison with the correlation function for the 

original data (cf. the black curve in Figure 93) shows a good match. This match indicates that the 

sinusoidal trend dominates the correlation function of the original data. 

Figure 94. Correlation function for a cosine trend 

Note that the cosine transform is the interferogram for the data, and hence the method just 

described is simply the application of a filter in the interferogram domain. Since performing 

Fourier transforms introduces artifacts, a preferable method would be to apply the filter in the 

original interferogram data and then transform to get a trend-removed radiance time series. For 

non-stationary data, a method preferable to the Fourier method is the application of a wavelet 

filter. In summary, we have seen how important trend removal is in investigating small-scale 

structures in the data, as the trends can dominate the statistical measures, i.e. correlation functions 

and spectra. Of course, the question arises, “What is the trend that should be removed?” 

Obviously, this is an arbitrary process: Is a low-order polynomial or sinusoidal trend appropriate? 
103 

Over what sub-window lengths should the trend be calculated? Unfortunately, there is no onesize-fits all method, and it is then left to the analyst to decide. Typically, the statistical measures 

are calculated and then the analyst’s intuition is brought to bear. Often, this process reveals 

artifacts in the data or in the pre-processing methods. This same issue is discussed in a different 

context in the next section: fitting models to the radiance spectra. 

5.1.3.2 Fitting Models to the Radiance Power Spectra 

Figure 82 and Figure 83 show the results of fitting models to the radiance power spectrum. As 

mentioned above, the two models used are a quadratic function plus noise, , 

and a model often used in turbulence analysis, the von Karman function plus noise, 

(5.1.11)

Where, τνk is a temporal scale, σ

2

 is the variance of the radiance data. In this analysis, we are using 

the von Karman form for a scalar function. In the quadratic model there are two parameters to 

estimate, and in the von Karman model there are three. 

We use Mathematica’s FindFit routine to perform the fits. As in the trend removal process, there 

is some ambiguity in performing the fits; specifically, over what frequency points should the fit 

be performed? Since there is a well-defined noise floor (cf. Figure 82), and we include this as a fit 

parameter, we can use all the high-frequency points. Obviously, in the quadratic model case we 

do not want to use all the low frequency points – as the model grows without bound as f  0. 

(Recall, this is not intended to represent the physical phenomena, but rather to look for Brownian 

noise in the data.) As can be seen from Equation 5.1.8, the von Karman model reaches a constant 

as f  0, so we can use low-frequency points, but we must be careful when removing trends. As 

mentioned above, trend removal acts like a high-pass filter on the data, as can be seen in Figure 

83, so we do not want to use all the low frequency points. So, how does one choose which 

spectral points to include in the model fitting? We would like to use as many points as possible, 

but at the same time we do not want to bias the fits. 

Return to Figure 82. For that example we used the 10th through last points for the quadratic model 

and the 6th through last for the von Karman one. We see that the quadratic fit is better at the lowfrequencies, but worse at the high-frequencies. Consider using 10th through last for both. As seen 

in Figure 95, now the von Karman model does a poorer job at both ends of the frequency range. If 

we expand the von Karman model in a power series around zero, the first two terms are a 

constant and a negative quadratic (a – bf2

), which can be seen in Figure 95. Going the other way, 

Figure 96 shows the result of using the 2nd through last points in the fit. Obviously, neither model 

does a good job of fitting the higher-frequency points, but the von Karman is superior to the 

quadratic. It is not exactly clear why this fit did so poorly. One possibility is that the fitting 

routine is over-fitting to the lower-frequency (much larger in magnitude) points. 
104 

Figure 95. Same as in Figure 82, but using the 10th to last spectral points in fitting both models. 

Figure 96. Same as in Figure 82, but using the 2nd to last spectral points in fitting both models. 

Next, consider the de-trended data from Figure 83. In that case, we used the 10th through last and 

4th through last for the quadratic and von Karman models, respectively, to get good fits. Figure 97 

illustrates the results with using the 4th through last points for both models. In this case, the 

quadratic model does not do a good job fitting the data, similar to what happened with the 

original data. Using the 10th through last points for the von Karman model essentially is fitting the 

noise, as can be seen in Figure 98. 
105 

Figure 97. Same as in Figure 83 (de-trended data), but using the 4th through last points in the fits. 

Figure 98. Same as in Figure 83, but using the 10th to last points in the fits. 

We now turn to a case that was not dominated by a trend: wavenumber 2355 from the first 5000 

points during Data Set 2 on 15 Jan 2008. First we look at the original data. Figure 99 shows the 

fits using the 4th through last for the von Karman model and 10th through last for the quadratic 

one. The fits to both models are very good over their full fitting domain. In Figure 100, we use 

the 2nd though last points for the von Karman fit, and get a very good fit over all these points. 

Using the 4th through last for the quadratic fit, as seen in Figure 101, does not perform as well as 

using the 10th through last. Next, we consider the de-trended data for this case. Figure 102 shows 

the comparable fits as used in Figure 99. As expected, the von Karman fit is quite good and the 

quadratic model does quite well over its set of fit points (10th through last). Since the de-trending 

mainly affected only the 1st frequency bin (cf. Figure 85), one would expect that changing the 

fitting domains, as was done for the original data, does not change the results in this case. 
106 

Figure 99. Model fits using the 4th through last points for the von Karman, and 10th through last for 

the quadratic. 

Figure 100. Same as Figure 99, but using the 2nd through last points for the von Karman fit. 

Figure 101. Same as Figure 99, but using the 4th through last points for both model fits. 
107 

Figure 102. Same as Figure 99, but for the de-trended data. 

In summary, we have seen that for the situation with a significant trend, the set of frequency 

points used in the fits has significant effects on the quality of the fit. This is especially true 

between the original and de-trended data. Moreover, as expected, data without much of a trend is 

less sensitive to the choice of points used in the fits. More importantly, the von Karman model 

provided much better fits to the data in the small-trend case than could the quadratic. This was not 

the situation for the data with the large linear trend, where we could get good fits with both 

models for the original and de-trended data. The situation with the case that had the large 

sinusoidal trend is similar, as can be seen from Figure 103 and Figure 104 (original and detrended data), where the fit points for both models in both figures was the 10th through last. These 

issues with the fits could hint at an underlying data quality issue with data that has large trends. 

Figure 103. Spectrum and fits for the data shown in Figure 88 (large sinusoidal trend). Fit points are 

10th through last for both models. 
108 

Figure 104. Same as in Figure 103 but for de-trended data. Fit points were 10th through last for both 

models. 

5.1.3.3 Looking for Turbulence in the Radiance Data 

Since there are over 1700 wavenumbers, trying to find cases that had good fits to the von Karman 

turbulence spectral model by looking at spectra is problematic. As a first-cut look through the 

data, the following methodology was used. If we consider pure noise, we would expect that the 

second and subsequent values in the correlation function would be very small. (Recall that due to 

normalization, the first point is always one.) On the other extreme, with highly correlated data, 

the second and subsequent points would be close to one. We have seen this with the artificial 

correlation due to trends. In the middle, as with smaller-scale turbulence, one would expect a 

somewhat smooth decay from larger correlation values to smaller ones. Hence, the quick-look 

method consisted of overlaying the 2nd, 10th, and 100th lags in the correlation domain and looking 

for this decaying structure. There are a few regions in the wavenumber domain where this 

structure is visible. In fact, the one around 900 cm-1 is where the large trends described above 

originate. Figure 106 shows the correlation values for the original data, where we can see the 

contaminating effects due to the trends. Looking at these types of plots with the original data was 

how the trends were first noticed. Figure 107 and Figure 108 show the same type of plots for the 

data set that had the large sinusoidal trends at small wavenumbers. The first figure is for the 

original data and the second is for the quadratic-fit method de-trended data. As opposed to the 

first case (cf. Figure 105 and Figure 106), where the quadratic trend removal makes for a 

significant difference, the same trend removal applied to the case with sinusoidal trends does not. 

Hence, using these plots is also helpful in finding other data issues, in this case a sinusoidal trend. 
109 

Figure 105. Correlation function at three different lags, for all wavenumbers, for de-trended data. 

Figure 106. Same as Figure 105, but for the original data. 

Figure 107. Correlation plot for the case with the large sinusoidal trends (cf. Figure 88). 
110 

Figure 108. Same as Figure 107, but for the (quadratic) de-trended data. 

Another way of looking for these correlation structures is to look at the full correlation function 

for all the lags. Figure 109 illustrates this for the original data from 15 Jan 2008 (Data Set 1) for 

all lags and for all wavenumbers. The color-coding uses hot colors for positive correlation and 

cool ones for negative. The data superimposed is the radiance for the first time sample and is 

presented for reference. The artificial correlation due to the trends is obvious at the smaller 

wavenumbers. Figure 110 shows the same type of presentation, but for the de-trended data. In this 

case, it is very difficult to see much in the way of interesting structure. However, if one looks 

very closely at the bottom of the plot, at around wavenumbers 900, 1500, and 2300, regions of 

elevated correlation can be seen. Figure 111 is the same data, but instead of showing all 5000 

lags, it only shows the first 50. At this scale, the correlation structure is now revealed. This is why 

the 2, 10, and 100 lag plots were used for the initial investigations; it was much easier to find the 

“interesting” regions in these lags. 
111 

Figure 109. Correlation plot for all lags and all wavenumbers. The color scale uses hot colors for 

positive values and cool colors for negative ones. 

Figure 110. Same as Figure 109, but for de-trended data. 
112 

Figure 111. Same as in Figure 110, but only for the first 50 data samples (approximately 15 seconds). 

5.1.3.4 Are We Seeing Turbulence? 

The next question is, “Do the correlation function plots presented above imply that turbulence 

was being measured?” We can compare these plots for a day that was known to be turbulent, and 

one that was calm. Recall that January 15 and 17, 2008 were in the former category, and January 

14, 2008 was in the latter. In the following, we present the correlation plots for the de-trended 

data covering two time periods on the 14th (Figure 112 and Figure 113), four on the 15th (Figure 

114 through Figure 117), and two on the 17th (Figure 118 and Figure 119). Some of these plots 

have already been shown above, but we repeat them here so all the relevant figures are in the 

same place. The first, and most obvious difference is that there is no correlation structure in the 

900 cm-1 region on January 14, whereas all the other cases do show a structure, except for January 

15, 2008, Data Set 2, 1st 5000 points (Figure 116). Second, the magnitude of the correlations seen 

in the 1500 and 2300 cm-1 regions is larger on the turbulent days than on the calm day. However, 

the differences are not dramatic. 
113 

Figure 112. Correlation plot for 14 Jan 2008, Data Set 1, second 5000 points.

Figure 113. Correlation plot for 14 Jan 2008, Data Set 2. 

Figure 114. Correlation plot from 15 Jan 2008, Data Set 1, second 5000 points.
114 

Figure 115. Correlation plot from 15 Jan 2008, Data Set 1, third 5000 points 

Figure 116. Correlation plot from 15 Jan 2008, Data Set 2, first 5000 points. 

Figure 117. Correlation plot from 15 Jan 2008, Data Set 2, second 5000 points. 
115 

Figure 118. Correlation plot from 17 Jan 2008, Data Set 1, first 5000 points.

Figure 119. Correlation plot from 17 Jan 2008, Data Set 3. 

Next, consider power spectra and model fits for selected wavenumbers on the three days. (As 

with the correlation plots, some of the subsequent spectral plots have been shown above, but we 

include them here for ease of comparison.) We have chosen wavenumbers that correspond to the 

three regions of interest: around 900, 1500, and 2350 cm-1. In each case, the von Karman model is 

fit from the 2nd through last frequency points, and the quadratic model uses the 10th through last. 

First, consider spectra from January 14, 2008, Data Set 1. From the correlation plots (Figure 112 

and Figure 113), it can be seen that the data around 900 cm-1 is noise-dominated, and so there is 

no need to look at spectral plots in that range (except to verify that the data is noise-like) – there 

is no information contained in them. However, the spectra from wavenumbers 1504 and 2355, 

seen in Figure 120 and Figure 121, do show a good match to the von Karman model. The 1504 

wavenumber power spectrum has an order of magnitude worth of data above the noise floor, 

whereas the 2355 wavenumber power spectrum is less pronounced. As might be expected from 

the correlation plot for January 14, 2008, Data Set 2 (Figure 113), there is not much signature 

above the noise in the power spectrum. (Note that the scales for these and the subsequent power 

spectral plots are not necessarily the same. It is best to consider the height of the low frequency 
116 

portions above the noise floor seen in the higher frequencies.) The data from wavenumber 2355 is 

noise-dominated, whereas there is a very weak signature at wavenumber 1520 (Figure 122), 

although it could easily be argued that this is not statistically significant. 

Figure 120. Power spectrum and fits for wavenumber 1504 from 14 Jan 2008, Data Set 1, 2nd 5000 

points. 

Figure 121. Power spectrum and fits for wavenumber 2355 from 14 Jan 2008, Data Set 1, 2nd 5000 

points. 

Figure 122. Power spectrum and fits for wavenumber 1520 from 14 Jan 2008, Data Set 2, 1st 5000 

points. 
117 

The term “statistically significant” in this context is explained as such: As we have seen above, 

the distribution functions for the de-trended radiances are well-matched to a Gaussian one. If one 

takes the power spectrum of Gaussian-distributed data, it has an exponential distribution. (Do not 

confuse what we are discussing here with “Gaussian white noise.” In the latter case, the data are 

uncorrelated, whereas in this case they can be.) That is, at each frequency, the power spectral 

level is distributed exponentially. Specifically, if we had multiple independent radiance time 

series that came from the same Gaussian distribution, then the spectral level at each frequency 

would represent a sample from an exponential distribution with mean and standard deviation 

values equal to the spectral level (definition of an exponential distribution). Unfortunately, this 

means that the power spectrum at each frequency has 100% noise – the standard deviation is 

equal to the mean. Therefore, at the lower frequencies, where the spectral level is higher, the 

errors are larger in magnitude (though not in percentage). It is important to note that in this 

context, the term “noise” refers to atmospheric fluctuations, not instrument errors. The latter is 

typically uncorrelated, and gives rise to the white spectrum at the higher frequencies. (We are 

speaking theoretically here: we are not considering the case where there are correlated sources of 

error as we have seen above with trends.) In the power spectra presented here, spectral averaging 

was performed (i.e. 9 spectra were averaged for a 5000 point window), and so the standard 

deviation is less than the mean. Nevertheless, one must be cautious in the interpretation of power 

spectra, and the associated fits, given the previous discussion. 

Consider now the data from January 15, 2008. As mentioned above, on this day there were 

numerous pilot and automated reports of turbulence over the mountains and on the lee-side of the 

mountains. We look at four time periods on this day: Data Set 1, 2nd and 3rd 5000 points (Figure 

123 through Figure 128); and Data Set 2, 1st and 2nd 5000 points (Figure 129 through Figure 132). 

As seen in the correlation plots, there is more “activity” in the wavenumber regions of interest. 

Most of the spectra are similar in that there is approximately an order of magnitude of signal 

above the noise, and there are good fits to the von Karman model. Comparing similar plots from 

the 17th (Figure 133 through Figure 136), we see consistent findings with the other days. A good 

comparison between January 14th and January 15th is seen in Figure 121 and Figure 128. Both are 

for wavenumber 2355 and are on the same scale. It can be seen that the data from the 15th shows 

much more pronounced spectral amplitude at the lower frequencies. Both cases illustrate good fits 

to the von Karman model. Is this a sign of more turbulence on the 15th? The evidence seems 

consistent with that hypothesis, but at it is not conclusive. 

 
118 

Figure 123. Power spectrum and fits for wavenumber 961 from 15 Jan 2008, Data Set 1, 2nd 5000 

points. 

Figure 124. Power spectrum and fits for wavenumber 1504 from 15 Jan 2008, Data Set 1, 2nd 5000 

points. 

Figure 125. Power spectrum and fits for wavenumber 2355 from 15 Jan 2008, Data Set 1, 2nd 5000 

points. 
119 

Figure 126. Power spectrum and fits for wavenumber 961 from 15 Jan 2008, Data Set 1, 3rd 5000 

points. 

Figure 127. Power spectrum and fits for wavenumber 1504 from 15 Jan 2008, Data Set 1, 3rd 5000 

points. 

Figure 128. Power spectrum and fits for wavenumber 2355 from 15 Jan 2008, Data Set 1, 3rd 5000 

points. 
120 

Figure 129. Power spectrum and fits for wavenumber 1504 from 15 Jan 2008, Data Set 2, 1st 5000 

points. 

Figure 130. Power spectrum and fits for wavenumber 2355 from 15 Jan 2008, Data Set 2, 1st 5000 

points. 

Figure 131. Power spectrum and fits for wavenumber 1504 from 15 Jan 2008, Data Set 2, 2nd 5000 

points. 
121 

Figure 132. Power spectrum and fits for wavenumber 2354 from 15 Jan 2008, Data Set 2, 2nd 5000 

points. 

Figure 133. Power spectrum and fits for wavenumber 1504 from 17 Jan 2008, Data Set 1, 1st 5000 

points. 

Figure 134. Power spectrum and fits for wavenumber 2354 from 17 Jan 2008, Data Set 1, 1st 5000 

points. 
122 

Figure 135. Power spectrum and fits for wavenumber 1504 from 17 Jan 2008, Data Set 3. 

Figure 136. Power spectrum and fits for wavenumber 2354 from 17 Jan 2008, Data Set 3. 

5.1.4 WRF Model: March 6, 2004 

WRF data (temperature and H2O profiles), generated for an actual lee wave turbulence event on 

March 6, 2004, were used in all simulations conducted in anticipation of the MRS field test. For 

the WRF data used, the center of turbulence is assumed to be located at 39.5°N, 105.0°W, and at 

an altitude of 5 km, where the largest perturbations of the air’s vertical velocity and water vapor 

were observed (see Figure 137). 
123 

(a) 

(b) 

Figure 137. Horizontal (a) and vertical (b) cross-sections of vertical velocity and H2O respectively for 

the lee wave turbulence, March 6, 2004. 

5.1.5 FLI-FM Simulations 

FLI Hyper-Cam imagery was simulated with FLI-FM in order to develop testing requirements for 

the MRS field experiment. These requirements include determination of spectral resolution and 

viewing geometry dependence. This simulation involved using WRF data (temperature and H2O 

profiles) generated for an actual lee wave turbulence event and radiosonde data to simulate the 

anticipated experimental observations. 
124 

5.1.5.1 Spectral Resolution Dependence 

FLI-FM calculations using the March 6, 2004 WRF model were performed for a limited portion 

of the Hyper-Cam array for its apodized spectral resolutions of 1, 4, and 16 cm-1. The viewing 

point was located at 40.03253°N, 105.53775°W, and 2.9 km elevation to match the exact 

coordinates of the MRS field experiment. The image size was assumed to be 5 x 25 

vertical/horizontal pixels with a single pixel angular size equal to 0.01 rad (~0.57°). Thus, the full 

angular image size is equal to 0.05 x 0.25 rad (~2.9° x 14.3°) in vertical/horizontal directions 

respectively. 

FLI radiances were simulated for a northward and a westward viewing Hyper-Cam at the three 

spectral resolutions and at five distances from the turbulence center and the brightness 

temperature computed. In the following figures, D is this distance and is measured in terms of D0, 

the maximum distance from the observer to the turbulence center. 

Figure 138. Spectral resolution dependence on lee-wave turbulence signal for a northward viewing 

Hyper-Cam. Lower right hand panel shows an image of the turbulence as would be viewed at 835 

cm-1 by the Hyper-Cam operated at the lowest spectral resolution of 16 cm-1. 
125 

Figure 139. Spectral resolution dependence on lee-wave turbulence signal for a westward viewing 

Hyper-Cam. Lower right hand panel shows an image of the turbulence as would be viewed at 835 

cm-1 by a Hyper-Cam operated at the lowest spectral resolution of 16 cm-1. 

As shown in Figure 138 and Figure 139, the wavenumber channels near 835 cm-1 have the highest 

variance due to lee wave turbulence, regardless of spectral resolution. It can also be seen that the 

northward viewing instrument, which resolves the wave amplitude, has a signal about four times 

larger than a westward viewing instrument that is looking into the wave train, and therefore is 

only sensitive to the turbulence along the axis of the lee wave train. The important conclusion to 

be drawn from these calculations is that the Hyper-Cam should be able to detect lee wave 

turbulence, even when operated at its lowest spectral resolution. 

5.1.5.2 Elevation Angle and Distance Dependence 

Next, the standard deviations for the five elevation angles and 25 azimuth angles (i.e. the 5 x 25 

pixel image) for the five distances from the center of the lee waves were computed. The central 

pixel (3, 13) of the image was directed straight to the SSE (local azimuth angle 157.5°, clockwise 

from North) with an elevation angle +5° above the horizontal. The elevation angle was chosen so 

that the bottom rays in the image do not touch the surface (the real terrain height of WRF data is 

taken into account). These simulations used the March 6, 2004 WRF model. 

One hundred twenty-five FLI radiances total were computed with FLI-FM in the range 

800–1300 cm-1 with 1 cm-1 spectral resolution and no apodization applied. For a given spectral 
126 

channel, the statistics were computed by averaging over all 125 image pixels in three different 

ways: 

1. The mean brightness temperature and its standard deviation were computed (Figure 140 

and Figure 141); 

2. The standard deviation for the brightness temperature when the corresponding mean 

vertical profile, obtained by averaging over azimuth angles, was subtracted (Figure 142, 

this variability is referred under as ΔBTVER); and 

3. The standard deviation for brightness temperature over the image when the left 

neighboring column was subtracted (Figure 143, this variability is referred under as 

ΔBTHOR). 

Figure 140. Mean brightness temperature spectrum, averaged over all image pixels. 

Figure 141. Standard deviation from the mean brightness temperature spectrum. 

wavenumber, 1/cm

800 900 1000 1100 1200 1300

BT, K

140

160

180

200

220

240

260

280

wavenumber, 1/cm

800 900 1000 1100 1200 1300

BT, K 0

2

4

6

8

10
127 

Figure 142. Standard deviation of the quantity ΔBTVER (see text). The enlarged plot shows channels 

chosen for visualization. 

Figure 143. Standard deviation of the quantity ΔBTHOR (see text). 

Although ΔBTHOR is about one order of magnitude lower than ΔBTVER, as shown in Figure 142

and Figure 143, the two results highly correlate with each other and their spectral dependence is 

very similar (Figure 144). It was found that an on-line spectral channel Δ = 957 cm-1 (H2O) has 

the maximum variability for both ΔBTs. Figure 145 shows images for this channel processed in 

the three ways as described above. Despite the high correlation between standard deviations for 

both ΔBTHOR and ΔBTVER, the images of these quantities look very different and have a 

significantly different BT scale. For comparison, Figure 146 shows the same panels for spectral 

channel Δ = 933 cm-1, which is representative of an off-line window frequency showing “regular” 

variability. One can note that the value of the signal on all three panels varies in wide limits (see 
128 

corresponding legends), depending on whether an on-line or off-line channel is selected, but the 

corresponding pictures (i.e. relative variations) look almost the same. 

Figure 144. Standard deviation of ΔBTHOR versus standard deviation of ΔBTVER.

Figure 145. Imagery for the on-line 957 cm-1 spectral channel, which has maximum variability for 

both ΔBT.
129 

Figure 146. Imagery for the off-line channel 933 cm-1: the “regular” variability for both ΔBT.

These simulations show that the FLI has the potential to observe the varying atmospheric state 

(temperature and H2O) associated with mountain waves. 

The simulations were then performed with the same March 6, 2004 WRF model for seven 

observer locations total, for a larger 35 x 35 pixel image. The first observer location was directly 

under the center of turbulence (the elevation angle for the central image pixel is equal to 90°). 

The next three observer locations were east of the turbulence center, while the main looking 

direction was to the west, with elevation angles equal to 60°, 30°, and 12° respectively. Finally, 

the last three observer locations were north of the turbulence center and the main looking 

direction is to the south with the same set of 3 elevation angles. Observer coordinates were 

chosen in such a way that the central ray of the image went exactly through the center of 

turbulence. Thus, the smaller elevation angle corresponds to the larger distances to the turbulence 

center and vice versa. West/south-looking directions allowed viewing of the turbulence from 

different sides: either ahead or from the side of an aircraft path. 

Each simulated image contains 35 x 35 rays around the main viewing direction. The angular size 

of each pixel was set to 0.01 rad, which corresponds approximately to a 20° full field of view 
130 

(± 10° from the main viewing direction, horizontally and vertically). Table 8 and Figure 147 

below explain the viewing geometry of the simulations. 

Table 8. Viewing geometry for the simulations. 

Location 

# 

Central 

viewing 

azimuth angle, 

α0 (degrees) 

Central 

viewing 

zenith angle, 

β0 (degrees) 

Observer 

Latitude 

(°N) 

Observer 

Longitude 

(°W) 

Distance to 

turbulent 

center (km) 

Wavenumber 

of max RMS 

(cm-1) 

1 -- 90 39.5 105.0 2.0 1093 

2 270 (West) 60 39.5 104.99 2.31 1213 

3 270 (West) 30 39.5 104.96 4.0 957 

4 270 (West) 12 39.5 104.87 9.62 957 

5 180 (South) 60 39.51 105.0 2.31 1247 

6 180 (South) 30 39.53 105.0 4.0 1078 

7 180 (South) 12 39.60 105.0 9.62 888 

Figure 147. Layout of the viewing geometry fields of view for the simulations. 

FLI radiances were computed with FLI-FM in the 800-1300 cm-1 range with spectral resolution 

equal to 1 cm-1, and strong Gaussian apodization was applied for an interferometer with assumed 

maxOPD = 0.5 cm. Brightness temperature radiances were used for further analysis. 

For each observer location, two atmospheric states were used for the simulations (when doing ray 

tracing for imagery). The first one was the actual WRF data (with lee wave turbulent 

perturbations), while the second one was an averaged “calm” atmospheric state for that day, 

obtained by horizontal averaging of WRF data. Corresponding sets of spectra are RPERT(ν,α,β,n)

and R0(ν,α,β,n), where R is the brightness temperature in K, ν is the wavenumber, α and β are the 

horizontal/ vertical pixels of each image, and n = 1:7 is the observer location index (from Table 

8). We analyze the value of ΔR=RPERT - R0 to simulate the difference between calm and turbulent 

conditions. 
131 

Figure 148 through Figure 154 show statistics of the signal obtained by averaging over all α and β

for each of the seven observer locations separately. The upper panel is the mean value of the 

referenced spectra R0; the middle panel is the mean value of the difference ΔR=RPERT -R0, and the 

bottom panel is the RMS-difference for ΔR. The green arrows on the bottom panels show 

channels for which the RMS-difference is maximum (Table 8 shows these channel wavenumbers 

in the rightmost column). The full images of ΔR for the same selected channels are shown in 

Figure 155 through Figure 161. 

Figure 148. Statistics of the radiance signal at observer location no. 1. 

Figure 149. Statistics of the radiance signal at observer location no. 2. 
132 

Figure 150. Statistics of the radiance signal at observer location no. 3. 

Figure 151. Statistics of the radiance signal at observer location no. 4. 

Figure 152. Statistics of the radiance signal at observer location no. 5. 
133 

Figure 153. Statistics of the radiance signal at observer location no. 6. 

Figure 154. Statistics of the radiance signal at observer location no. 7. 

Figure 155 through Figure 161 below show the images of ΔR=RPERT - R0 for the channels shown 

by green arrows on Figure 148 through Figure 154 (which represent maximum RMS) for seven 

observer locations. Legends to the right of the images provide the color scale of the brightness 

temperature differences, K. 
134 

Figure 155. Modeled BTD image at observer location no. 1, α0 = 270° (West), β0 = 90°. 

Figure 156. Modeled BTD image at observer location no. 2, α0 = 270° (West), β0 = 60°. 

Figure 157. Modeled BTD image at observer location no. 3, α0 = 270° (West), β0 = 30°. 
135 

Figure 158. Modeled BTD image at observer location no. 4 , α0 = 270° (West), β0 = 12°. 

Figure 159. Modeled BTD image at observer location no. 5, α0 = 180° (South), β0 = 60°. 

Figure 160. Modeled BTD image at observer location no. 6, α0 = 180° (South), β0 = 30°. 
136 

Figure 161. Modeled BTD image at observer location no. 7, α0 = 180° (South), β0 = 12°. 

As can be seen from Figure 148 through Figure 161, the lee wave mountain turbulence produces 

strong signals in comparison with the radiance computed for a background calm atmospheric 

state. The maximum signal is observed on the water lines and it is obviously caused by 

updrafts/downdrafts providing sharp water vapor variations. Note that by analyzing ΔR, the 

angular variations of radiances caused by different ray trajectories in the field of view are 

significantly reduced. The actual images of ΔR and selected channels (Figure 155 through Figure 

161) depend mainly on the viewing geometry and on how much of the lee wave is in the 

instrument’s FOV. The simulation results indicate that mountain wave turbulence should be 

detectable with the Hyper-Cam at the Colorado MRS, if the WRF model is an accurate 

representation of conditions and the simulation is an accurate representation of what can be 

observed. After the field test, it was determined that the high signals observed were most likely an 

artifact of the model itself; this is discussed in further detail in Section 5.5. 

Another simulation was made at the same observation point with the March 6, 2004 WRF model. 

The image size in this case was set at 25 vertical x 35 horizontal pixels with a single pixel angular 

size of 0.01 rad (~0.57°); the full angular image size was equal to 0.25 x 0.35 rad (~14.3° x 20.1°) 

in vertical/azimuthal directions respectively. The image central pixel (13, 18) was always directed 

straight to the east (local azimuth angle 90° from the North clockwise), i.e. to the center of the 

observed lee wave. The viewing elevation angles considered (measured at the central pixel of the 

array) were 90°, 60°, 45°, 30°, and 15° above the horizon. Eight hundred and seventy five 

(25*35) FLI radiance spectra were computed with the FLI-FM for each of the five different 

viewing angles. The wavenumber range was 650 – 1650 cm-1 with a spectral resolution of 16 cm-1

and no apodization applied. 

For each spectral channel, the RMS (root mean square) and peak-to-peak differences were 

computed over all 875 image pixels for each measurement frame. Each radiance spectrum was 

subtracted from the azimuthal (horizontal) radiance mean spectrum. The results are presented in 

terms of brightness temperature. These statistics are shown in Figure 162 and Figure 163. One 

can note that although the spectral shapes of both statistical characteristics are similar, the 

peak-to-peak values are about order of magnitude greater than the RMS values (e.g. 12K and 1K, 
137 

respectively). It is also worth to noting that RMS difference regularly increases with decreasing 

elevation angle due to longer path through the lee wave turbulence-disturbed atmospheric state. 

Similar statistical displays were produced from the actual Hyper-Cam data in order to be able 

assess, in real-time, the lee wave information content of the measurements. These statistics are 

shown in Section 5.3.5. 

Figure 162. Brightness temperature RMS difference over all image pixels when mean azimuthal 

brightness temperature is subtracted. 

Figure 163. Brightness temperature peak-to-peak difference over all image pixels when mean 

azimuthal brightness temperature is subtracted. 

wavenumber, cm-1

600 800 1000 1200 1400 1600

BT, K

0.0

0.2

0.4

0.6

0.8

1.0 o

o

o

o

o

!!!

wavenumber, cm-1

600 800 1000 1200 1400 1600

BT, K

0

2

4

6

8

10

12

o

o

o

o

o

!!

ea to c d e e ce o e t e age e a ut a ea s subt acted
138 

The maximum signal is observed at νMAX = 826 cm-1 for all elevation angles except β0 = 60°, for 

which νMAX = 1098 cm -1, which might be explained by the fact that in the later case (β0 = 60°), the 

center of the lee wave is located directly in the field of view, while for other elevation angles we 

observe less turbulent atmospheric regions surrounding the region of lee wave turbulence. 

Figure 164 through Figure 168 show the expected signal (BTD from the azimuthal mean) for all 

five chosen elevation angles and for the channels with maximum BT variations across the field of 

view. Note that the scale is limited to ± 0.5 K in order to accentuate the smaller and larger signals 

beyond these limits; this scale is well the Hyper-Cam noise limits (see Appendix A, which shows 

noise floor at 16 cm-1 resolution is equivalent to 0.16 K BTD at 300 K). The results of these 

simulations show that large signals (i.e. 1 K or more) should be observed for the case of a welldeveloped mountain-generated turbulent wave. 

Figure 164. BTD from the azimuthal mean for ν = 826 cm-1 and β0 = 90°. 

Figure 165. BTD from the azimuthal mean for ν = 1098 cm-1 and β0 = 60°. 

-0.5 

-0.4 

-0.3 

-0.2 

-0.1 

0.0 

0.1 

0.2 

0.3 

0.4 

0.5 

(Azimuth ) deg

-8 -6 -4 -2 0 2 4 6 8

(Elevation angle ) deg

-6

-4

-2

0

2

4

6

(Azimuth ) deg

-8 -6 -4 -2 0 2 4 6 8

(Elevation angle ) deg

-6

-4

-2

0

2

4

6

-0.5 

-0.4 

-0.3 

-0.2 

-0.1 

0.0 

0.1 

0.2 

0.3 

0.4 

0.5 
139 

Figure 166. BTD from the azimuthal mean for ν = 826 cm-1 and β0 = 45°. 

Figure 167. BTD from the azimuthal mean for ν = 826 cm-1 and β0 = 30°. 

Figure 168. BTD from the azimuthal mean for ν = 826 cm-1 and β0 = 15°. 

(Azimuth ) deg

-8 -6 -4 -2 0 2 4 6 8

(Elevation angle ) deg

-6

-4

-2

0

2

4

6

-0.5 

-0.4 

-0.3 

-0.2 

-0.1 

0.0 

0.1 

0.2 

0.3 

0.4 

0.5 

(Azimuth ) deg

-8 -6 -4 -2 0 2 4 6 8

(Elevation angle ) deg

-6

-4

-2

0

2

4

6

-0.5 

-0.4 

-0.3 

-0.2 

-0.1 

0.0 

0.1 

0.2 

0.3 

0.4 

0.5 

(Azimuth ) deg

-8 -6 -4 -2 0 2 4 6 8

(Elevation angle ) deg

-6

-4

-2

0

2

4

6

-0.5 

-0.4 

-0.3 

-0.2 

-0.1 

0.0 

0.1 

0.2 

0.3 

0.4 

0.5 
140 

5.2 Mountain Research Station Data Collection Activity 

The Mountain Research Station (MRS) is an interdisciplinary research facility of the Institute of 

Arctic and Alpine Research, University of Colorado, located on the front range of the Rocky 

Mountains. Research at the MRS is primarily performed by investigators not formally associated 

with the MRS. The support facilities of the MRS include laboratories, offices, classrooms, cabins, 

a dining hall, and a bathhouse. Laboratories include the Kiowa wet-chemistry lab, several plant, 

soil, and chemistry labs within the larger John W. Marr Laboratory Building, and the Tundra Lab 

located at 3500 m elevation on Niwot Ridge. 

The test site at MRS was located at an elevation of 2,900 m (9,500 ft), approximately 76 km 

(47 mi) WNW of Denver International Airport (DEN). This elevation and clear line of sight 

towards the airport was an ideal location for observing mountain waves that form over the 

Rockies. Figure 169 shows a simple graphical representation of the viewing geometry from the 

MRS test location to the atmosphere above DEN. Details on the test location are given in 5.2.2, 

the MRS Data Acquisition section. 

Figure 169. Representation of test location at MRS looking at atmosphere over DEN. 

5.2.1 MRS Data Collection Preparations 

As mentioned above, the University of Colorado’s Mountain Research Station (MRS) includes 

three sites with buildings including the base camp (elevation 9,500 ft.), the Tundra Lab (11,600 

ft.), and the C1 site at an intermediate elevation. On March 14, 2011, two of the research team 

members visited the MRS near Ward, CO, to investigate potential viewing sites for radiometric 

observations of mountain waves and associated air turbulence. Each site has differing clearviewing directions and difficulties of access. All sites have electrical power. The highest site, the 

Tundra Lab, has an unimpeded 180-degree view from the east to the south and around to the 

west. Lower sites have east and west views, and the base camp has a clear view of Pikes Peak, as 

well as a view to the east. The Tundra Lab provides panoramic viewing but is accessible only by 

tracked snow vehicle for much of the year. The base camp also has Internet access and is served 

by both UPS and FedEx. A photograph of the Tundra Lab and its location relative to Boulder, CO 

is given in Figure 170. 
141 

 Figure 170. Mountain Research Station Tundra Site (left) and Map from Boulder, CO (right). 

The attractive features of the MRS for the FLI project are that it provides long, near-horizontal 

views from several sites that all have plenty of electric power, and that the base camp has all 

utilities and amenities including high-speed internet and living quarters. The road to the base 

camp is kept plowed all winter. The rooftop platform on the base camp building is reached by a 

combination of an extension ladder and a steel stairway, as shown in Figure 171, below. 

Figure 171. Rooftop platform on base camp building and access ladder/stairs. 

The platform provides excellent views to the south and east, but it is small and it is already in use 

by two other sets of instruments. Furthermore, it would provide no shelter for our instruments, 

and the access method would make it very difficult to install the Hyper-Cam. Nevertheless, it is 

shown here to illustrate the viewing available at MRS. Figure 172 is a photo of Pikes Peak, which 

is a 14,110 ft. peak 86 miles SSE of the MRS. 
142 

Figure 172. Pike’s Peak, at a distance of 86 miles from the MRS. 

After touring the base camp, we travelled by snow vehicle to the C1 site, where again we were 

shown a rooftop on a small shelter where we could mount equipment on tripods. The C1 

buildings are in the background of the vehicle photo shown in Figure 173. 

Figure 173. The snow vehicle at the C1 site. 

On the way back, we investigated an open clearing as a potential site. Several photos are shown 

below to illustrate the views that the site provides. Long views to both east and west are available 

in the clearing and at nearby locations. Electric power is available. Snow does not accumulate 

here due to high winds, which makes it an inhospitable place to work. 

Pike’s Peak
143 

Figure 174. View to the west from clearing, Kiowa Peak (13,156 ft. elevation). 

Figure 175. View to the east from clearing. 

The annual weather cycle at MRS constrained the test dates and locations. Mountain waves are a 

winter phenomenon [31]. During summer, the road to the C1 and Tundra Lab sites is open for 

wheeled vehicles. After a snowpack has formed in the winter, access to the higher elevations is 

provided by the snow vehicle. At an intermediate time, there is not enough snow for the tracked 

vehicle and access is not available, except to the base camp. During September-October, it is 

usually possible to drive to the C1 site. The upshot of these considerations is that the best test 

dates are late October to early November. 
144 

Figure 176. Leanne West and Kurt Chowanski, Kiowa Peak ~6 miles to the west. 

We discussed the site visit and test dates with Larry Cornman of the STAR Institute and Bob 

Sharman, who confirmed the wintertime nature of mountain waves and made several points: 

 Mountain waves can be forecast no more than three days in advance. 

 Turbulence associated with mountain waves is over the terrain, not east of it. (It 

propagates upward.) 

 Looking south might be good because our line of site would cross flights paths 

into DEN. 

 Mountain waves persist all day, so aircraft data and PIREPS constitute good truth 

data. 

 We can get turbulence data from http://weather.aero/. NCAR archives actual 

airline turbulence data. 

 Bob and Larry spoke about using NCAR’s connections in the airline industry to 

obtain additional PIREPS and aircraft turbulence data to support the MSR 

measurements, for example by getting United Airlines on board. 

 Large waves condense out the water vapor, which could present a problem for a 

clear line of sight, but perhaps we could look horizontally through a dry slot. 

In addition, the location of the tests, on the front range of the Rocky Mountains, was important 

for acquiring truth data due to the amount of air traffic in the area: flights into, out of, and at 

cruise altitudes over the Denver International Airport. The large amount of air traffic meant that 

pilot reports (PIREPS) and automated reports from United Airlines (UAL) 757 and Delta Airlines 

(DL) 737 aircraft were available as “ground truth” for the turbulence detection efforts. 

The UAL and DL aircraft are equipped with instruments to estimate and automatically report 

eddy dissipation rates (EDR) to the one-third power. These reports consist of peak and mean 

value of the EDR over the previous one minute (in cruise). These reports cover “none” to 

“extreme” levels of turbulence, and they were used as verification data. The truth data allows for 

the evaluation of both true detections and false alarms by the sensor and algorithms. Pilot reports, 

on the other hand, are more subjective in nature. PIREPS are not always given for turbulence 
145 

encounters (even severe ones); they rarely provide for null turbulence; the reported time and 

position can be in error; and the intensity level of the turbulence reported by the pilot is a 

subjective measure of the actual intensity level. Nevertheless, these data, when available, are still 

valuable in the verification process. Figure 177 shows the flight tracks of the EDR-equipped UAL 

757s over a 24-hour period. There are many flights in the Colorado area. Figure 178 shows the 

same data for the DL 737 aircraft. While there are fewer DL flights over Colorado, data from 

these aircraft were helpful in the verification process. 

Figure 177. UAL 757 reports over a 24 hour period. 

Figure 178. DAL 737 reports over a 24 hour period. 
146 

Our test plan for the mountain wave measurements in Colorado included two radiometric 

instruments, the D&P TurboFT spectroradiometer and the Telops Hyper-Cam imaging 

interferometric radiometer. During the test plan review on August 24, 2011, the procedure for 

pointing the D&P and the Telops instruments in the same direction was identified as an issue. 

A consensus emerged that the instruments should be on a common mount and boresighted. This 

decision had several implications, one of which was that the D&P must operate in the same 

outdoor environment as the Hyper-Cam. The manufacturer’s specified operating temperature 

range for the D&P is about 60 – 90°F, so a temperature-controlled shelter was designed and 

constructed for the D&P. The shelter design also considered: 

1. Both instruments can currently be mounted individually on heavy-duty tripods, with dovetail 

slides. It was desirable to retain this tripod mounting capability. 

2. The D&P is very susceptible to electromagnetic interference (EMI), so the shelter should 

provide EMI shielding for it. 

3. The shelter must provide access to the D&P’s built-in aiming sight, the liquid nitrogen refill 

port, and the calibration blackbody. 

To meet all of these requirements, we purchased a commercial enclosure made of lightweight 

aluminum, along with a thermostatically-controlled forced-air heater for the interior of the 

enclosure and foam insulation to line the interior of the enclosure. This combination of 

components provides both thermal stability and EMI shielding. 

We modeled all of these elements, along with the D&P spectrometer itself in SolidWorks 

mechanical design software. A cutaway drawing of the system is shown in the Figure 179. The 

blue box in the figure is the enclosure heater. Access to the D&P is provided by means of the 

hinged lid, and a viewing port on the front provides a view of the outside world. 
147 

Figure 179. SolidWorks illustration of the D&P instrument installed in its shelter with the heater 

(blue box). 

The D&P was bolted through the bottom of the enclosure to a base plate. The base plate allows 

for the system to be mounted on a tripod or in an over-and-under mount with the Hyper-Cam. The 

base plate also provides a mount for a turning mirror to the front of the system (not shown). By 

using the mirror, we can aim the D&P field of view vertically, which is another capability that 

was identified during the test plan review. A boresighted camera was added to the enclosure to 

provide a more convenient aiming procedure and visible images. 

For the D&P/Hyper-Cam common mount, the desirability of an over-and-under configuration 

was also discussed during the test plan review with the following requirements: 

1. Both instruments should attach with the same dovetail slides that are used on the tripods. 

2. The mount must provide access to the Hyper-Cam visible camera focus knob. 

3. The mount must provide for boresight adjustment. 

4. The mount must provide elevation angles in the range 0 – 60 degrees. 

5. The mechanical design should include consideration of ease of assembly and teardown, 

minimizing weight, and disassembly for shipping. 

A low-cost means of achieving all of these requirements is illustrated in Figure 180. The entire 

framework is fabricated from bolted Uni-Strut members and gussets. The Hyper-Cam mounts on 

the bottom, and the D&P shelter mounts on the top because it is lighter and access to the D&P is 

required for liquid nitrogen. The elevation axis is at the center of gravity of the moving parts, and 

an elevation lock is provided. This mount enabled the field test team to aim the instrument out a 

window in the dining hall at MRS, as required. 
148 

Figure 180. Scale drawing of radiometric instruments on an over-and-under mount. 

A SolidWorks representation of the mount with the D&P in a temperature-controlled box with the 

Hyper-Cam mounted below is shown in Figure 181. 

Figure 181. A SolidWorks representation of the mount with the Hyper-Cam and D&P installed. 

Note the location of the rotation axis in Figure 181; this rotation enables the operator to adjust the 

elevation angle while maintaining boresight of the two sensors. The alignment of the boresight 

was tested by setting up two light bulbs at a 19-inch vertical spacing, which is equivalent to the 

distance between the D&P’s and Hyper-Cam’s optical axes. Near perfect alignment was 

observed, even after a complete teardown and re-assembly. 
149 

The mount itself is shown below in Figure 182; the D&P is encased in the aluminum enclosure 

and the Hyper-Cam is mounted below. 

Figure 182. The final mount for the D&P and Hyper-Cam. 

In addition to the construction of the enclosures and mounts in preparation for the field test, the 

sensors themselves were checked. Ten-meter cables were ordered from Telops for the 

Hyper-Cam. This longer length enables the user to not be right next to the instrument while 

running it. The operation was tested with the longer cable and the wide-FOV lens (25.6° x 30.6° 

FOV when the entire FPA is used). The lens was delivered with the original Hyper-Cam order but 

had not been used in a field test until the November 2011 MRS experiment. 

The D&P was sent back to the manufacturer in early September 2011 for a refurbishment. This 

service included re-conditioning the detector, re-pumping the Dewar, and a check of all systems. 

The detector was re-aligned and the signal restored to that of a new instrument. Despite this 

refurbishment, we still experienced problems with a loose wire that caused errors in the collected 

data. The data issues are described in 5.3.3, the Data Analysis section. 

The D&P enclosure was tested in an environmental chamber on the Georgia Tech campus. The 

chamber has a controller that reports several temperatures in real time, as well as a graph showing 

their time history. These temperatures are as follows: 

Set Point: the temperature that the operator has selected for the chamber, 

D (Duct) A – T: the air temperature in the duct leading to the chamber, and 

R (Room) A – T: the air temperature in the chamber. 

The room temperature was about 7 °C at the start of the test. However, the duct air temperature 

was about –1 °C, and all of the cold air emerged forcefully from a ceiling vent in one corner of 

the room. The D&P system was installed on a table near the ceiling vent. The heater thermostat in 

the enclosure was set to 21 °C, the lid closed, and the enclosure placed under the blast of –1 °C 

air. All temperature monitoring was done with the D&P; it reports box temperature and the 
150 

instrument temperature continuously on the GUI, and it also records these values in file headers 

during data acquisition. 

The four temperatures monitored by the D&P are ambient temperate (Tbox), instrument 

temperature (Tinst), blackbody temperature (Tbb), and laser diode temperature (Tld). Those 

recordings, along with comments, are included in Table 9 and Figure 183. All temperatures are in 

degrees Celsius. 

Table 9. D&P monitored temperatures (°C). 

Elapsed Time (minutes) Tbox Tinst Tbb Tld Comment 

0 22.0 18.9 13.3 24.8 

5 21.1 19.4 19.0 24.5 

11 19.4 19.0 18.8 24.7 

13 19.4 19.0 18.5 24.7 Heater unplugged 

15 19.4 18.9 18.2 24.7 Cold air blower unexpectedly turned off 

20 19.2 18.9 17.6 24.7 

25 19.0 18.9 17.1 24.7 Cold air blower unexpectedly turned on 

30 18.8. 18.9 16.7 24.7 

33 18.7 18.9 16.4 24.7 Heater plugged back in 

38 18.1 18.8 15.8 24.7 Heater temp. set to ~38 °C, turns on 

40 19.9 19.5 16.3 24.7 

42 24.5 20.1 20.1 24.9 

44 25.9 20.3 22.9 24.9 Though Tbox–Tinst >5 °C, no alarm 

47 26.3 20.4 24.9 24.9 

 

Figure 183. The D&P temperature readings during the time of the test. 

From the table and graph we note several things: 

1. When being blasted with –1 °C air, the box lost heat at a rate that corresponds to 

somewhere from 2 to 4 °C in 30 minutes. 

2. During the time interval from 10 to 40 minutes, Tinst was constant. 

3. When the heater came on, it caused a temperature rise of ~8 °C in 5 minutes. 

4. The sudden, large change in Tbox caused a change of >1 °C in Tinst. 
151 

The conclusions from this test were as follows: 

1. The D&P enclosure allowed easy access for filling the cryostat with LN2. 

2. The D&P enclosure allowed easy access for changing the fore-optics and installing the 

insulating sleeve. 

3. The heater had plenty of power. 

4. Hot air from the heater did not cause adhesion or melting problems for the box’s 

insulation. 

5. The thermostat set point was inaccurate (set at 21 °C, but it never came on). 

6. The thermostat ΔT was very large, ~8 °C. 

7. We needed to provide better temperature control in the box. 

The results of the D&P enclosure test required that a better temperature controller be incorporated 

into the box. The new components were purchased and a new controller installed for the heater. 

These tests were repeated and the new heater performed as needed. 

5.2.2 MRS Data Acquisition 

Knowing that weather is very unpredictable with any type of lead time, the STAR team was 

watching for weather patterns that indicated high winds/turbulence. With only 3 days’ notice, the 

GTRI team went to MRS based on STAR’s prediction that high winds were likely. We conducted 

the field campaign during four days in November 2011. Several sites at elevations higher than the 

main lab were selected for possible locations for the data collection; however, due to snow cover 

and an inoperational Sno-Cat at the time of the test, the data collection was confined to the dining 

hall and parking lot at MRS. However, this restriction did not inhibit the data collection; looking 

out of an open window from the second floor of the dining hall provided a clear view of the sky 

over the tree line in the direction of Denver (shown in Figure 184), even though the instruments 

were below the tree line. Data collection activities are outlined below in Table 10 and Table 11. 

Figure 184. Instrument set-up looking out the open window of the MRS dining hall (left panel), and 

instruments looking ESE toward Denver (right panel). 
152 

Table 10. Data Collection Matrix, Atmospheric Observations. 

Time 

(UTC) 

Azimuth 

(deg) 

Elevation 

(deg) 

HyperCam 

FOV 

Run 

Number 

Duration 

(min) D&P HyperCam 

HyperCam 

Resolution 

(cm-1) 

Location 

Wednesday, November 16 

18:57 113 10.1 WFOV 5 30 Yes Yes 16 Inside 

19:40 113 29.4 WFOV 6 30 Yes Yes 16 Inside 

20:58 148 40 WFOV 7 30 Yes Yes 16 Inside 

22:29 148 40 WFOV 9 30 Yes Yes 1 Inside 

23:14 148 40 WFOV 10 30 Yes Yes 16 Inside 

Thursday, November 17 

15:53 ? ? WFOV 1 30 No Yes 16 Inside 

17:45 164 10.6 WFOV 2 30 Yes Yes 16 Inside 

18:12 112 10.1 WFOV 3 30 Yes Yes 16 Inside 

18:48 112 4 NFOV 4 30 Yes Yes 16 Inside 

20:26 164 13.5 NFOV 5 30 Yes Yes 16 Inside 

21:05 164 39 NFOV 6 30 Yes Yes 16 Inside 

21:38 164 39 WFOV 7 30 Yes Yes 16 Inside 

22:12 164 25 WFOV 8 30 Yes Yes 16 Inside 

22:49 176 18 WFOV 9 30 Yes Yes 16 Inside 

Friday, November 18 

15:45 0 88.5 WFOV 1 30 No Yes 16 Outside 

16:38 0 88.5 WFOV 2 30 No Yes 4 Outside 

17:45 112 12 WFOV 3 30 Yes Yes 16 Outside 

18:20 112 31.5 WFOV 4 30 Yes Yes 16 Inside 

18:55 117 31.5 WFOV 5 30 Yes Yes 16 Inside 

19:40 117 19 WFOV 6 30 Yes Yes 32 Inside 

20:54 258 24 WFOV 7 10 Yes Yes 16 Inside 

21:17 85 20 WFOV 8 30 Yes Yes 1 Inside 

21:52 123 11.5 WFOV 9 33 Yes Yes 1 Inside 

Sunday, November 20 

16:14 175 31 WFOV 1 30 No Yes 16 Inside 

16:51 190 30 WFOV 2 30 No Yes 16 Inside 

17:35 113 13 WFOV 3 30 Yes Yes 16 Inside 

 
153 

Table 11. Data Collection Matrix, Surface Emissivity Observations, Resolution = 1 cm-1 for all runs. 

Time 

(UTC) Scene Run 

Distance 

to Scene 

(m) 

Viewing 

azimuth 

(deg) 

Air 

Temp 

(°C) 

Object 

Object 

Temp 

Before (°C) 

Object 

Temp After 

(°C) 

17:06 powdery 

snow 

1 5.9 87 1.2 

gold plate 1.9 1.4 

dry 

concrete 1.6 1.9 

wet 

concrete 1.4 1.2 

disturbed 

snow 

0.2 0.2 

undisturbe

d snow 0 0.2 

17:12 powdery 

snow 

2 5.9 87 2.3 

gold plate 1.4 1.4 

dry 

concrete 1.9 1.7 

wet 

concrete 1.2 1.4 

disturbed 

snow 

0.2 0.2 

undisturbe

d snow 0.2 0 

17:16 powdery 

snow 

3 5.9 87 2.2 

gold plate 1.4 1.2 

dry 

concrete 1.7 1.4 

wet 

concrete 1.4 0.9 

disturbed 

snow 

0.2 0.3 

undisturbe

d snow 0 0 

17:20 powdery 

snow 

4 5.9 87 2.1 

gold plate 1.2 1.3 

dry 

concrete 1.4 1.7 

wet 

concrete 0.9 1.2 

disturbed 

snow 

0.3 0.3 

undisturbe

d snow 0 0.2 

17:33 snow bank 1 28.7 185 2.5 gold plate 0.2 0.8 

snow -1.2 -1.7 

xx ice/snow 1 21.4 218 xx 

gold plate -0.2 -0.7 

compact 

snow 

-2.3 -2.5 

powdery 

snow 

-3.5 -3.0 

ice patch -1.8 -1.8 

xx rock/snow 1 9 178 1.3 

gold plate -0.7 -0.5 

powdery 

snow 

-2.1 -1.9 

ice patch -0.7 -0.9 
154 

The weather conditions on Thursday, November 17 were exactly as desired – lenticular clouds 

(indicating mountain waves; see Figure 185) with clear skies otherwise and many PIREPS 

indicating turbulence. We collected several hours of data in many directions, including the 

direction of reported turbulence. 

Figure 185. Lenticular clouds, Thursday November 17, 2011.

Winds were calmer, but still present on Friday. Saturday was snowy and overcast, so we took the 

opportunity to collect data for runway surface emissivity. Sunday was calm, but cloudy with a 

few patches of clear sky. We collected data before needing to pack the instruments, trying to 

replicate some of the viewing angles from Thursday. The D&P was experiencing issues both 

Saturday and Sunday. The Hyper-Cam worked well the entire test.

Approximately 13 hours of data were collected in all. During the field activity, data was copied to 

external hard drives to ensure multiple copies of the data were available. Supporting data 

collected includes meteorological data from C1 (provided by MRS), radiosonde data, MODIS 

data, EDRs, and PIREPS. Samples of MODIS data from November 17 and PIREPs data from 

November 16 are given in Figure 186 and Figure 187, respectively. In these examples, the 

MODIS data show the mountain waves, and the PIREPS show moderate to light turbulence. 

Figure 186. Aqua MODIS water vapor channel data; November 17, 2011; mountain waves visible. 

Band 27 (6.535 – 6.895 micron). 
155 

Figure 187. PIREPs data from November 16, 2011; moderate turbulence reported. 

Code has been developed to read the PIREPS data, which were provided by UCAR in netCDF 

format. These data were converted to Excel with Matlab to make it easier to read. The data 

chosen to include in the Excel spreadsheets distributed to team members only included PIREPS 

with data in the turbulence fields and PIREPS from times during the test. The spreadsheets were 

then sorted according to severity of turbulence; 5 (Moderate to Severe) was the highest turbulence 

intensity reported during the time of the data collect. These spreadsheets were then utilized to 

determine which data runs had the best chance of having captured turbulent events. The latitude 

and longitude of the PIREPS observation must be correlated to the viewing angle of the HyperCam and D&P during different measurement runs. Additionally, the Eddy Dissipation Rate 

(EDR) reports were compared with these PIREPS. 
156 

Figure 188. GOES water vapor channel (top panel), experimental downslope wind product (middle 

panel), and model wind at 500 hPa (bottom panel), November 18, 2011. Colorbar scales and units for 

each are beneath each subfigure. 
157 

5.3 Mountain Research Station Data Analysis 

Hyper-Cam and D&P data were analyzed in detail in an attempt to reveal mountain lee wave 

turbulence and associated CAT. A variety of analysis techniques have been applied to the data. 

Turbulence should cause fluctuations in temperature and water vapor structures due to updrafts 

and downdrafts associated with mountain waves that are visible in the LWIR. 

5.3.1 Hyper-Cam/D&P Data Comparison 

The Hyper-Cam and D&P instruments were boresighted and operated simultaneously, so we are 

able to check the agreement between their radiance measurements. The radiance of Hyper-Cam 

pixel (176, 120) was used for this comparison, because that pixel location was determined to be 

the D&P aimpoint during an onsite alignment check. D&P data were recorded at 4 cm-1

resolution, and unfortunately only one Hyper-Cam run was recorded at this resolution, during a 

time when the the D&P was not operating. Hyper-Cam 1 cm-1 resolution data shown in Figure 

189 was downsampled to match the D&P 4 cm-1 resolution data. 

Figure 189. Telops radiance (1 cm-1 resolution) vs. D&P radiance (4 cm-1 resolution). 

The data shown in Figure 189 were measured at MRS on November 16 at 22:31 UTC and the sky 

viewing direction for this data run was 148° azimuth, 40° elevation. Figure 189 shows the 

radiance spectra have the same shape, but the Hyper-Cam radiance is lower than the D&P 

radiance. 

5.3.2 D&P Calibration Issues 

The D&P experienced problems in the field during the latter portion of the acquisition activity. It 

was operative during the day with lenticular clouds, November 17. It was operative intermittently 

after that, but the blackbodies were not responding, so the later data is not calibrated. 
158 

An unexpected result was seen while investigating the D&P data from the MRS data collection. 

Sample interferograms were collected at 4 coadds and blackbody interferograms were collected at 

1,000 coadds, meaning that each resulting spectrum is the average of 4 or 1,000 spectra, 

respectively. When each interferogram was viewed separately, it was noted that for both the 

sample and cold blackbody data, every even interferogram in the sequence exhibited a waviness 

about zero OPD. This phenomenon was not seen in the warm blackbody data. A normal 

interferogram and a wavy interferogram are shown in Figure 190. 

Figure 190. D&P interferograms. The green interferogram exhibits waviness about zero OPD that is 

seen in all even-numbered interferograms. 

These data were sent to D&P in an effort to understand what the source of this result could be, 

and Winthrop Wadsworth of D&P provided an explanation. There is a slight mismatch in the 

frequency content of the two orientations of the rotor that produces the four interferograms per 

revolution. One set of two interferograms is produced when the rotor is parallel to the 

beamsplitter, and the other set of two is produced when the rotor is perpendicular to the 

beamsplitter. There is an AC couple in the signal chain that takes out a low frequency signal from 

the rotor that is due to blocking the IR beam in between interferograms. It is adjusted to minimize 

this low frequency signal without affecting the long wavelength end of the spectra. The waviness 

is in the even-numbered interferograms, but it is not of concern because the signal is not in the 

frequency range of the IR spectrum. The effect shows up more on lower temperature spectra 

because it has a fixed amplitude, so it is a higher percentage of the signal when looking at lower 

signal amplitudes. Because the waviness in the interferogram is outside the frequency range of 

our spectra, it should not affect our data. 

5.3.3 Analysis of D&P Data 

STAR identified several issues with data discontinuities and spikes in the D&P data; however, a 

plan was developed to mitigate these issues in subsequent analysis. Prior to the MRS field test, 
159 

the D&P instrument had been sent back to the manufacturer for refurbishment to make sure it was 

in good condition; many of the issues, however, were not noticed until the analysis. 

An investigation was undertaken into data quality issues related to the D&P radiometer data taken 

at the MRS. Two separate issues were seen: data spikes and data gaps. Figure 191 illustrates both 

of these problems. The data gaps in this image are seen as data discontinuities – not spikes. These 

occur because the radiance level (or calibration) changes over time, so if the sensor is not 

collecting data for a while, and then comes back, a discontinuity in the radiance can occur. A 

good example of this can be seen around sample 15000 in Figure 191. The large discontinuities 

seen in this figure are not of major concern, because they occur between datasets. Each dataset 

was not only separated in time from the next but also was recorded at different azimuth/elevation 

angles. The smaller data gaps within each dataset are not apparent in this figure but are explained 

in more detail within this section; the data spikes are quite obvious in this figure. The major 

concern is that when calculating statistical characteristics of the data, such as correlation 

functions or power spectra, these data quality problems can have significant effects. Two 

approaches are possible. For the data gaps, one may just use segments of the data that are 

continuous, or have very short temporal gaps. For the spikes, one may use a spike removal 

algorithm. 

Figure 191. Example of spikes in the radiance time series (ν = 936 cm-1). 

Figure 192 is similar to Figure 191, except that radiance is shown at 1501 cm-1, instead of 936 

cm-1. These data are over the same time period, so we can see that the temporal discontinuities are 

quite different at different wavenumbers. However, the spikes are still visible. 
160 

Figure 192. Same as previous figure, but for ν = 1501 cm-1. 

Figure 193 and Figure 194 illustrate these differences over wavenumber. Figure 193 shows every 

5th wavenumber over the first 300 wavenumbers. The color-coding is such that the “hotter” colors 

show more deviation and the black regions are ones where the deviations are above a chosen 

threshold. Figure 194 is similar, except that every 10th wavenumber over the first 1000 (out of a 

total of 1728) are shown. At higher wavenumbers, there are numerous large variations just due to 

the radiances being noise-dominated. The larger radiance jumps in the temporal discontinuities 

can be seen as the vertical black stripes. Certain wavenumbers also exhibit large variations as a 

function of time. These can be seen as the horizontal black stripes. 

Figure 193. Sample-to-sample differences in the radiance time series. The vertical axis index 

corresponds to every fifth wavenumber index for the first 300 wavenumbers; the horizontal axis 

index corresponds to the sample number index in the time series. 
161 

Figure 194. Same as previous figure, but the vertical axis index corresponds to every tenth 

wavenumber index for the first 1000 wavenumbers; the horizontal axis index corresponds to the 

sample number index in the time series. 

Figure 195 shows the result of a simple spike detection and mitigation algorithm. The original 

data is in black and the “fixed” data is in red. The technique uses the deviation from a local 

median, normalized by the local median deviation, to create a standardized variable. These values 

are then compared against a threshold to determine if the datum is a “spike.” If it is a spike, then 

it is replaced by the local median, which appears to work well for this data, although more testing 

and perhaps some refinement is needed. 

Figure 195. Spike removal example. 
162 

Figure 196 shows the temporal differences (i.e. the sample-to-sample difference) as a function of 

sample number. It can be seen that most of the temporal differences are small and clustered 

around the nominal sampling interval. However, some large deviations are apparent, and it 

appears that the deviations get worse towards the end of the recording interval. This deviation 

may be the result of a problem with wiring in the sensor, something that was not noticed until 

after the data collection. Figure 197 is similar to Figure 196, except that the vertical scale only 

goes from zero to one to give better resolution around the “nominal” sampling interval. Figure 

198 is similar to Figure 197, except that a small set of temporal samples (around the large jump 

towards the right-hand side of Figure 196) is shown. It can be seen that the time gaps are 

bifurcated in an odd fashion, something that is quite apparent in Figure 199, which is a further 

blow-up of Figure 198. This problem in the data sampling interval indicates the sensor is not 

stable. 

Figure 196. Time gaps as a function of sample number. 

Figure 197. Same as previous figure, but linear vertical scale – and limited to zero to one. 
163 

Figure 198. Same as the previous figure, but zoomed in on samples 65000 to 70000. 

Figure 199. Same as previous figure, but zoomed in from 67200 to 67600. 

Finally, Figure 200 illustrates the sample-to-sample temporal deviations (log scale) on the vertical 

axis versus the sample-to-sample radiance differences. This analysis was performed to see if there 

is a correlation between the two different data quality issues. Most of the points are in the lower 

left-hand corner, which indicate the variation in the sensor-sampling interval mentioned above. 

The single point spikes are seen along the bottom of the figure, and the large temporal deviation 

is seen in the upper portion. The deviations are well-separated is good because this means that the 

mitigation methods mentioned previously (spike removal and using regions that do not contain 

large temporal gaps) are feasible. 
164 

Figure 200. Temporal versus radiance differences. 

As mentioned above, the spike removal algorithm did a good job of removing the spikes. The 

next step was to deal with the uneven sampling rate. Two approaches were investigated: 

1. The use of spectral analysis methods that can handle unevenly spaced samples; and 

2. The removal of samples that were locally discontinuous, followed by the methods in (1). 

Figure 201 shows the sample-to-sample differences over the sample range 84000 to 85200 on 

November 17, 2011. Figure 202 shows the distribution of differences over the same sample 

range. Compare this to Figure 203, which shows the distribution for 37500 to 46000. There is a 

much wider distribution of sampling rates in the 84000 to 85200 period. (See also Figure 196 and 

Figure 197.) By locally discontinuous, we do not mean the large changes as seen in the middle of 

Figure 199, but rather those seen in Figure 204. This latter figure shows that the sampling rate 

was nominally around 0.25 seconds, but every 10th sample there is a two-point discontinuity in 

the rate. The two-point discontinuity is not a universal feature of the data, as three-point ones 

were also seen. Figure 205 and Figure 206 show mixed two- and three-point, and three-point 

behavior, respectively, as well as illustrate much larger discontinuities than seen in Figure 204. 

Also associated with these discontinuities is a repeated data (radiance) value at the beginning of 

the sampling discontinuity. This repeated value can be seen in Figure 207, where the sampling 

rate (offset by -0.25 sec.) is shown in black, and sample-to-sample 

(spike-removed/trend-removed) radiance differences are shown in red. The repeated radiance 

values show up as zeroes. (The offset is merely a convenience to help illustrate the location of the 

zeroes relative to the discontinuities.) The two repeated radiance values are aligned with the 

beginning of the two-point sampling rate discontinuity. Figure 208 is an interesting case in that 

the temporal discontinuities are not associated with repeated radiance values. Figure 209 

illustrates three-point temporal discontinuities where there were still only two repeated samples. 

In each of these three cases, the temporal discontinuities were at every 10th point; this is a 

dominant feature, though not the rule, as can be seen in Figure 210, which shows the log of the 

number of samples between repeated data values. 
165 

Figure 201. Sample-to-sample temporal differences for the sampling period 80400 to 85200.

Figure 202. Histogram of sample-to-sample time differences for samples 80400 to 85200.
166 

Figure 203. Same as Figure 202, but for the sample range 37500 to 46000. 

Figure 204. Sample-to-sample temporal differences, showing “local” discontinuities in the sampling 

rate. Note that the predominant discontinuity is two samples long for samples in the 80400 to 80500 

period. 
167 

Figure 205. Same as in Figure 204 but for the sample range 83000 to 83100. Note the much larger 

discontinuities in sampling rates, as well as the three-point discontinuities. 

Figure 206. Same as in Figure 204, but showing a mixture of two- and three-point discontinuities in 

the sampling rate. Sample interval is 84750 to 84850. 
168 

Figure 207. Sample-to-sample temporal differences (offset by -0.25 sec) in black, with sample-tosample data difference values in red. Note the repeated data values are the zeros at the beginning of 

each discontinuity in sampling rate. The sample range is 80400 to 80450. 

Figure 208. Same as Figure 207, except that in this case the temporal discontinuities are not 

associated with repeated values. The sample range is 83000 to 83050. (Offset for sample-to-sample 

temporal differences is -0.4 in this case.) 
169 

Figure 209. Same as Figure 207, but for a three-point discontinuity case. The sample range is 84800 

to 84850. (Offset for sample-to-sample temporal differences is -0.42 in this case.) 

Figure 210. Log-histogram of the number of samples between repeated data values.

As mentioned above, there were two approaches taken to dealing with the uneven sampling rates. 

Both relied on a method to compute a power spectrum, or more precisely a periodogram estimate 

of the power spectrum, that accommodates uneven sampling. This method is known in the 

literature as the Lomb-Scargle Periodogram. The standard Fourier periodogram is the dot product 

of the data vector with sines and cosines at different frequencies, i.e. the Discrete Fourier 

Transform (DFT). The Fast Fourier Transform (FFT) is a very efficient implementation of this 

type of periodogram, but requires constant sampling rates. For a Gaussian random process, the 
170 

standard periodogram is exponentially-distributed. The Lomb-Scargle periodogram maintains the 

same statistical distribution and is also equivalent to a least-squares fitting of the data to 

sinusoids. It also reduces to the standard periodogram when the data is evenly spaced. These facts 

make the Lomb-Scargle periodogram a very attractive method for spectral analysis 

of unevenly-spaced data. 

The Lomb-Scargle periodogram for data X(t) is given by, 

(5.3.1)

Where, 

(5.3.2)

It should be noted that this periodogram can be calculated for any set of frequencies ω = 2πf; that 

is, it is not restricted to fi = i/NΔt as in the FFT. This means that we can oversample the 

frequencies. Of course, one can do this for a standard DFT for equally spaced samples; but since 

the FFT implementation of the DFT is the predominant tool in spectral analysis, we use this 

method for comparison. For the FFT, however, the only way to increase the frequency resolution 

(for a fixed sampling rate) is to increase the number of samples. 

The first approach used in dealing with the unevenly spaced data was to apply the 

Lomb-Scargle periodogram directly to the radiance data. These results (in red) are compared to 

the standard periodogram (black), as seen in Figure 211, Figure 212, and Figure 213. Since the 

choice of frequencies to use in the Lomb-Scargle periodogram is somewhat arbitrary, we chose to 

use the same frequencies as in the Fourier spectrum, but oversampled by a factor of two. Since 

the data are not evenly sampled, we chose Δݐ to be the median of the sample-to-sample time 

differences. 

Figure 211, Figure 212, and Figure 213 show the Fourier and Lomb-Scargle periodograms 

at 913 cm-1 and the sample periods 80400–80900, 83000–83500, and 84000–84500, respectively. 

It can be seen that the Fourier and Lomb-Scargle spectra are fairly similar to each other in each 

case, and all are noise-like. As mentioned above, for both of these types of spectra the power at 

each frequency is distributed exponentially, which means that the standard deviation of the power 

at each frequency is equal to that power level, i.e. 100% error. To improve the estimates, spectral 

averaging is required. Figure 214 shows the result of averaging nine spectra over the full 

80400–85200 period. As with the individual spectra, the data are noise-like; however, a few 

systematic differences between the two types of spectra can be seen (e.g. at 0.06 Hz and 0.2–0.3 

Hz). Of course, averaging nine spectra only reduces the standard deviation by 1/ 9 = 1/3, which 

still means a 33% error. Hence, one must be cautious in making specific statements about the 

differences in the two types of spectra; nevertheless, they are different. 
171 

Figure 211. Lomb-Scargle periodogram (red) and standard Fourier periodogram (black) the sample 

interval 80400 to 80900, ν = 913 cm-1. The Lomb-Scargle periodogram has been oversampled by a 

factor of two over the Fourier one. 

Figure 212. Same as Figure 211, but for the sample interval 83000 to 83500. 
172 

Figure 213. Same as Figure 211, but for the sample interval 84000 to 84500. 

Figure 214. Same as Figure 211, but for spectra averaged over the whole 80400 to 85200 range. (Note 

the change in scale from the un-averaged spectra.) 

The second approach in dealing with the uneven sampling rate is to remove the sampling outliers 

and then process the data with the Lomb-Scargle periodogram. We used the same spike-removal 

algorithm to remove the “spikes” in the sample-to-sample temporal differences. The results of 
173 

this removal is seen in Figure 215, Figure 216, and Figure 217, which show the sample-to-sample 

temporal differences (black) and the “fixed” time differences for the periods 80400–80500, 

83000 –83100, and 84750–84850, respectively. This approach does an excellent job of removing 

the temporal “spikes.” Figure 218, Figure 219, and Figure 220 show the spike- and trendremoved radiances (black) and the “fixed” data for the same sample ranges. As we saw above, in 

the first and third sample periods, (with two- and three-point discontinuities, respectively), the 

temporal discontinuities were associated with repeated radiance values, and the fixed data now 

steps over those data points. We note, however, that the temporal spikes in some cases are also 

associated with data spikes, but not always. The second time period is more complicated. In the 

left-hand side of Figure 216, there are two temporal discontinuities that do not correspond to the 

otherwise regular 10-point repeating pattern. The spike removal algorithm does pick these 

discontinuities out, and as can be seen in Figure 219, these samples are associated with some odd 

features in the radiance data. 

Figure 221 through Figure 224 are the “fixed” data versions of Figure 211 through Figure 214. In 

black are the same Fourier spectra as shown before, and the blue curves are the Lomb-Scargle 

periodograms for the “fixed” data. Figure 225 compares the averaged Lomb-Scargle periodogram 

with the averaged “fixed” Lomb-Scargle periodogram. As with the “un-fixed” spectra, one can 

see some systematic differences in these spectra (e.g. 0.01–0.02 Hz, 0.04 and 0.08 Hz). 

Figure 215. Sample-to-sample time differences and “fixed” time differences for the period 80400 to 

80500. (Abscissa is time in seconds from sample 80400.) 
174 

Figure 216. Same as Figure 215, but for the period 83000 to 83100.

Figure 217. Same as Figure 215, but for the period 84750 to 84850. 
175 

Figure 218. Spike- and trend-removed radiances and “fixed” radiances for the period 80400 to 

80500. (Abscissa is time in seconds from sample 80400.) 

Figure 219. Same as Figure 218, but for the period 83000 to 83100.
176 

Figure 220. Same as Figure 218, except for the period 84750 to 84850. 

Figure 221. Same as Figure 211, but for the “fixed” data (blue).
177 

Figure 222. Same as Figure 212 but for the “fixed” data (blue).

Figure 223. Same as Figure 213, but for the “fixed” data (blue).
178 

Figure 224. Same as Figure 214, but for the “fixed” data (blue).

Figure 225. Averaged Lomb (red) and Averaged “fixed” Lomb (blue) periodogram for the sampling 

interval 80400 to 85200.

5.3.4 Investigation of “Amoeba” Pattern in Hyper-Cam Data 

Figure 226 shows results of the Hyper-Cam viewing the atmosphere at a low elevation angle from 

MRS on November 16 and 17. The upper left-hand image is that of the uncalibrated radiance 

when viewing one of the on-board blackbodies. There is a pattern in the instrument response of 

the focal plane detector array, called the “amoeba” pattern due to its shape. This artifact in the 
179 

spatial characteristics of the signal should not appear in the calibrated signal. However, one can 

see from the brightness temperature standard deviation plots for November 16 and 17 that this 

amoeba pattern seems to be present in the calibrated sky scene data, regardless of day or viewing 

angle. In Figure 226, the upper left panel is the uncalibrated detector output when viewing the 

ambient calibration blackbody whereas the other three panels show the standard deviation of 

brightness temperature when viewing the sky at a low elevation angle. The outline of trees can be 

seen in the field of view; they were fluctuating spatially due to high winds. 

Figure 226. Images of “amoeba” pattern in Hyper-Cam radiance for November 16 and 17, 2011. 

The temporal variability of the signal was investigated over 14 data cubes (# 8940–8953) from 

the November 17, 2011, Run 2 dataset. We computed the mean radiance, R0(ν,x,y), by averaging 

over 14 cubes for each wavenumber, ν, and each image pixel, (x,y). This mean radiance was then 

subtracted from the actual measured data. The obtained difference, ΔR, is the signal that is being 

analyzed. 

Figure 227 shows the spectrum of the standard deviation of ΔR averaged over all image pixels 

(320 x 160) for each data cube separately (represented by different colored lines). This figure 

therefore presents the total variability of the scene, which allows one to detect the spectral 

channels that vary most during the observation. The first and last 3 channels have been excluded 

from consideration since they suffer from wrap-around effect (i.e. the measured spectra are not 

apodized). 
180 

Figure 227. Standard deviation for radiance perturbations averaged over 320 x 160 image pixels, 

separately for 14 data cubes processed (November 17, 2011, cubes 8940–8953). 

Figure 228 shows an image of the standard deviation of ΔR for channel ν = 849.9 cm-1, the 

channel which showed the highest variability across image pixels. The image looks very similar 

to the “amoeba” pattern described above and shown in Figure 226. It confirms the necessity to 

understand the calibration issues. 

Figure 228. Standard deviation for radiance perturbations for channel #4 

(ν = 849.9 cm-1) that shows the highest variability across image pixels. 

The Telops Reveal Pro software that is used to control the Hyper-Cam during a measurement 

shows a real time update of the infrared image of the scene. As the screen updates, the software 

cycles through the wavenumber dimension of the hyperspectral data cube and randomly selects a 

radiance image to show on the display. This amoeba pattern is present on the display during 

calibration of the sensor when looking at the blackbodies. The presence of the pattern is not of 

concern here, as it is most likely representative of the detector response and should be removed 

during calibration. Why then, is it also apparent in the real-time display of the infrared scene? 

Further investigation revealed the answer. 

The Telops Reveal Pro software does a “quick calibration” on the data cubes for the display. The 

actual calibration, using the two blackbody measurements to calculate a gain/offset file to 

calibrate the entire set of measurements, is performed in the separate Telops program Reveal 

wavenumber

800 900 1000 1100 1200 1300

0.0000

0.0005

0.0010

0.0015

0.0020

0.0025

0.0005 

0.0006 

0.0007 

0.0008 

0.0009 
181 

Calibrate. This calibration is a post-process step that, for the data taken at MRS, took 

approximately three weeks to complete. For the most part, the calibrated radiance data obtained 

from this process does not show these amoeba patterns, and this is confirmed by comparing a 

blackbody interferogram image to a calibrated radiance image (i.e. the blackbody was measured 

as though it were the scene). Figure 229 shows the “amoeba” is calibrated out when comparing 

images of blackbody measurements before and after calibration. 

Figure 229. The interferogram image (left panel) and the radiance image (right panel) of the 

blackbody. 

There were occasions where the amoeba pattern was still seen in the radiance data as calibrated 

by post-processing. As the pattern is essentially the detector response, it should certainly be gone 

after this more thorough calibration. For the data where these patterns exist in the radiance data, 

the source of the problem is attributed to a lost calibration. The MRS datasets were taken in thirty 

minute intervals; blackbody calibrations were performed between each set. Therefore, one must 

assume that there is no sensor drift or changes in ambient condition during the thirty minutes that 

the data were taken. The Telops monitored values during a calibration and measurement are 

shown in Table 12. The data in Table 12 was from cube 8940 of the November 17, Run 2 dataset. 

Table 12. Telops monitored values during two blackbody and one scene measurement 

(all temperatures in Kelvin). 

 Blackbody 1 Blackbody 2 cube 8940 

FTSTemperatureMeasured 301.14 301.15 301.95 

IRLensTemperatureMeasured 301.43 301.44 301.42 

FPATemperatureMeasured 66.7 66.7 66.75 

AmbientTemperature 279.77 279.88 286.86 

EntranceWindowTemperature 285.82 285.87 290.46 

BeamsplitterTemperature 299.49 299.5 300.6 

FTSAmbientTemperature 300.01 300.06 301.38 

IRLens1Temperature 301.01 301.02 301.24 

CoolerColdFingerTemperature 306.36 306.42 310.77 

CoolerCompressorTemperature 321.72 321.72 321.72 

IRLensTubeTemperature 302.71 302.73 302.52 

EnclosureAmbientTemperature 297.44 297.39 300.98 

Ambient RH % 20 20 14 
182 

The two blackbody measurements were used to calibrate cube 8940. Note in Table 12 that there 

are several instances that indicated the sensor has drifted and the ambient conditions had changed. 

Cube 8940 was recorded 44 minutes after the blackbodies, and the calibration was no longer 

valid. Therefore, if the amoeba patterns are present in calibrated radiance data, the assumption is 

that it is due to a bad calibration and is not a sensor defect. 

This assumption was verified by Christoph Borel of the Air Force Institute of Technology (AFIT) 

in his presentation entitled, “Data processing and temperature-emissivity separation for tower 

based imaging Fourier transform spectrometer data” at the 2012 Telops Scientific Workshop. 

AFIT uses both the mid-wave and long-wave versions of the Hyper-Cam for testing temperature 

and emissivity (T&E) separation algorithms and analysis of combustion events, among other 

things, and for the most part, they do their own calibration rather than use the Telops-provided 

Reveal Calibrate. Dr. Borel’s presentation on T&E separation included a discussion of 

calibration, which included calculations of gains and offsets, bad pixel replacement, and flat-field 

correction. His figures of gain/offset maps were essentially our “amoeba” pattern. AFIT has 

termed this phenomenon the “Moiré” pattern, and as hypothesized, it represents the 

non-uniformity of the detector and can be calibrated out by measuring two blackbodies and 

generating gains and offsets to apply to scene interferograms. 

5.3.5 Temporal Variability Analysis Theory 

The algorithm used to identify potential turbulence in the Hyper-Cam data is based on the 

assumption that the natural (i.e. non-turbulent) azimuthal variations of observed radiances are 

negligible in comparison with natural variations due to the elevation angle. Thus, the average 

over 320 horizontal pixels generates a vertical profile of mean radiance that can be subtracted 

from observed data to reveal atmospheric inhomogeneity associated with mountain wave 

turbulence. It should be noted that all Hyper-Cam data are corrected for bad pixels; two methods 

are given in the following section. Results from the fourteen data cubes analyzed from November 

17 are shown here as well to illustrate the theoretical development of the variability analysis; 

detailed results on entire datasets are given in following sections. 

Temporal variability of the scene during one run is estimated by performing statistics over all 

data cubes for a given run after subtracting the row mean radiance for each cube/channel 

separately (to eliminate the radiance dependence on the viewing elevation angle). The 

computation of temporal variability ends up with one cube of data, which contains the estimated 

standard deviation for each pixel/channel separately. 

Figure 230 presents the vertical dependence of the observed radiances, obtained by horizontal 

averaging, for each of the 35 spectral channels (4 through 38). Figure 231 presents the 

corresponding vertical variability of the observed channel radiances around these means. 
183 

Figure 230. Mean vertical radiances; each successive channel shifted by 0.003 along the x-axis. 

Figure 231. Standard deviation for vertically averaged radiances; each channel shifted by 0.001 along 

the x-axis. 

From Figure 231, we can definitely see that something unusual appears in the upper part of the 

image (high variability in almost all channels). 

After the azimuthal average observed spectrum was calculated, it was subtracted from the actual 

radiances. Thus, for a given channel and given cube of data (i.e. time of observation) we can 

compute the RMS difference of the resulting deviations over the whole image matrix, which is 

160 vertical x 320 horizontal pixels. This spectral deviation, shown separately for each of the 14 

times that data cubes were available, is shown in Figure 232. When we average this RMS 

difference over all 14 observed cubes, we can find those channels of highest time variability. The 

radiance, W / m2 cm-1 str

0.00 0.02 0.04 0.06 0.08 0.10 0.12 Ver.Pixel #

radiance, W / m2 cm-1 str

0.00 0.01 0.02 0.03 Ver.Pixel #
184 

result of this averaging is presented in Figure 233. As can be seen, channels 5 (~863.18 cm-1) and 

28 (~1168.61 cm-1) show the most time variability, which in turn might be associated with 

mountain wave turbulence activity. 

Figure 232. Spectral RMS difference over 320 x 160 image pixels for channels 4 – 38 and 14 

consecutive data cubes (from the bottom to the top; each successive curve is shifted by +1.0e-5 

W/m2

cm-1sr along the Y-axis). 

Figure 233. Time variability (averaging over 14 observed data cubes) of the difference between 

observed and azimuthal (horizontal) mean radiance. Blue arrows indicate channels with maximal 

time variability, as shown in Figure 234 and Figure 235. 

The time development of the observed radiance disturbances for channels 5 and 28 is shown in 

Figure 234 and Figure 235, respectively. Although all previous statistical results have been 

obtained taking into account quality control (i.e. bad pixel replacement) described in the 

following section, the images in Figure 234 and Figure 235 show all pixels without regard to their 

quality control status, and results are displayed as 4 x 4 pixel averages for easier visualization. 

Also, the color scale is the same for all images, and it spans from –10 K to +10 K BTD, which is 

a measurable value range. Finally, it should be pointed out that the bright red spot visible at the 

upper left corner of all panels is caused by a cloud in the field of view. The dark blue stripe 

wavenumber, cm-1

900 1000 1100 1200 1300

0.00000

0.00001

0.00002

0.00003

0.00004

0.00005

0.00006

0.00007

0.00008

0.00009

0.00010

0.00011

0.00012

0.00013

0.00014

0.00015

wavenumber, cm -1

900 1000 1100 1200 1300

4.0e-5

4.1e-5

4.2e-5

4.3e-5

4.4e-5
185 

starting right after the red spot and spanning the whole azimuth range is most likely an artifact 

resulting from the abnormally high radiances due to cloud emission. 

Figure 234. Time development (every odd data cube from 1 to 14) of the BTD deviations from the 

vertical mean for observed Telops channel # 5, ~863.18 cm-1. 

Figure 235. Time development (every odd data cube from 1 to 14) of the BTD deviation from the 

vertical mean for observed Telops channel # 28, ~1168.61 cm-1. 
186 

5.3.6 Bad Pixel Map Creation 

It should be noted that in order to exclude “bad” (i.e. low responsivity) pixel observations, some 

data quality control needs to be performed. As is shown later in Figure 278, it is unlikely that 

either observed or simulated radiances exceed 0.04 W/m2

cm-1sr. All pixels for which the 

observed radiance exceeds this threshold value in one or more channels have therefore been 

excluded from the overall statistics. The threshold value of 0.04 was chosen after LBLRTM 

simulations and based on the first look of the MRS data as described previously. However, it 

should be noted that not all pixels marked as “bad” after this procedure are necessarily really bad 

pixels of the instrument, because the rule “<0.04” marks any accidental artifacts in the field of 

view (e.g. trees or the dining hall window frame, etc). Since the bad pixel map is defined for each 

run separately, we were able to compute the frequency of bad mapping, i.e. how many times each 

pixel is classified as bad one over all 7 runs. This frequency is shown in Figure 236: green pixels 

are bad in 4 runs out of 7 total; light blue, red, and black pixels are repeated as bad in 5, 6, and 7 

(all) runs correspondingly. These 97 + 63 + 63 + 645 = 868 pixels can be considered to be bad 

within the instrument detector array. Although the numbers of “bad” pixels which happen only in 

one, two, or three runs are much larger (6766, 11893, and 3088, respectively), they are mostly 

caused by the natural artifacts mentioned above, and for this reason are not shown in Figure 236. 

To illustrate this point, Figure 237 shows bad pixels observed in 2 runs out of 7. Thus, the simple 

rule “<0.04” works properly for identifying both bad pixels and naturally produced high radiance 

values. Those high radiance measurement pixels have been excluded from further consideration 

and do not distort the processing designed to detect lee wave turbulence. 

Figure 236. Frequency of pixels marked as bad over seven processed runs. 

0 64 128 192 256 320

0

64

128

192

256

in 4 7 runs, 

in 5  7 runs, 

in 6  7 runs, 

in 7  7 runs, 
187 

Figure 237. Bad pixels with frequency = 2 over seven runs, caused mostly by the artifacts in the field 

of view. 

5.3.7 Cloud Detection/Removal Algorithms 

Methods for accounting for the influence of clouds on FLI spectra were investigated and 

developed. These methods are needed in order to detect the relatively small atmospheric 

turbulence signals in the FLI radiance spectra. Three different techniques were defined: 

1. Cloud clearing: The time and/or spatial variation of radiance associated with cloud 

amount variations are used to separate the radiance coming from the clear air from that 

coming from the cloudy portions of the instrument’s field of view. 

 2. Cloud correcting: The cloud contribution to the observed radiance is estimated via a 

radiative transfer calculation in which the cloud emissivity spectrum and cloud distance 

from the instrument are estimated using the ratio of effective cloud amounts computed 

for two different spectral channels, one of which is a relatively clean atmospheric 

window transparent to the atmosphere between the instrument and the cloud, and another 

semi-transparent channel which is subject to the absorption of the atmospheric gas 

between the instrument and the cloud. The correct distance between the instrument and 

the cloud is specified as that distance in which the cloud amounts computed for both the 

transparent and semi-transparent spectral regions are the same. 

 3. Cloud detecting: The spectral features of clouds and clear sky are exploited to determine 

which parts of the scene are cloud and which are sky. 

“Cloud detecting” is a quick method of determination of cloud cover using information inherent 

in the scene. The “cloud clearing” technique assumes that the clouds are broken and that local 

variations of the observed radiance are due to variations in the cloud fractional coverage rather 

than to variations in the cloud height or their optical properties. The cloud clearing process only 

requires the radiance observations (i.e. no ancillary data or time consuming radiative transfer 

calculations are required). However, the technique does not work in a completely overcast sky 

condition. The cloud correction technique works for any cloud amount condition, including a 

dense overcast, but requires ancillary information on the temperature and moisture structure of 

0 64 128 192 256 320

0

64

128

192

256
188 

the atmosphere, as well as time-consuming radiative transfer calculations. As a result, the 

ultimate solution is a combination of these algorithms. 

5.3.7.1 Cloud Clearing 

The radiance measured by a spectrometer whose field of view contains a broken and or 

semi-transparent cloud can be expressed as 

(5.3.3)

where RCLD(ν,t) is the radiance coming from the cloud filled portion of the sensor’s field of view 

at spectral wavenumber ν and time t, RCLR(ν,t) is the radiance coming from the clear air within the 

sensor’s field of view, and N is the product of the fraction of cloud cover and the emissivity of the 

cloud. The clear air radiance can then be solved from the radiance observations at two different 

times, t1 and t2, if it can be assumed that the clear air radiance (i.e. atmospheric profiles) and the 

cloud altitude and optical properties are the same for the radiances measured at the two different 

times. The solution is: 

(5.3.4)

where: 

 (5.3.5)

In Equation 5.3.5 the wavenumber w is a reference channel window region wavenumber (or 

average of wavenumbers) where the clear air radiance, RCLR(w), is known from either surrounding 

time clear air window radiance observations or estimated from a clear sky radiative transfer 

calculation. Because the clear sky window radiance is very small (near zero from aircraft or high 

altitude observatory levels) compared to the spectral atmospheric radiances used for atmospheric 

turbulence detection, the result is not very sensitive to the accuracy of the assumed clear window 

radiance, RCLR(w). By using an average of radiance measurements over the large number of 

window wavenumbers within the spectrum, the impact of instrumental noise on the result can 

also be reduced. 

Figure 238 and Figure 239 show results obtained from ground-based ASSIST measurements 

obtained during the Chemistry and Physics Atmospheric Boundary Layer Experiment 

(CAPABLE) conducted during the June–August 2010 time period. The ASSIST data was 

transformed to the 16 cm-1 spectral resolution of the Hyper-Cam. The results shown are for two 

different days during CAPABLE, June 23 and July 6–7, 2010. As can be seen in the upper lefthand panel of each figure, clouds existed during both of these measurement days, with the 

heaviest cloud occurring near the end of June 23, 2010. 

The upper-left hand panel of each figure shows the radiance for the 1100–1150 cm-1 transparent 

window used as the reference channel to calculate the N*

 parameter used to 

cloud-clear the Hyper-Cam spectrum. Radiance is very small for this relatively transparent 

window, and the clear air radiance can be easily inferred by time interpolation of these clear air 

radiances across the obvious cloud contaminated fields of view. This reference channel clear air 

radiance is then used to define N*

 using Equation 5.3.5 which is used in Equation 5.3.4 to define 
189 

the clear air radiance for all the cloud-contaminated fields of view. Since the error in the 

calculated clear air radiance tends towards infinity as N*

 approaches unity, only values with an 

N* < 0.75 are retained, and the missing data are filled in by time interpolation of the acceptable 

clear air radiance values. In these cases, almost all the results shown are actual clear air radiance 

values, rather than being the result of time interpolation. 

Figure 238. Cloud-clearing results for June 23, 2010. The upper left panel shows the reference 

window region and cloud flag while the upper right, lower left, and lower right hand panels show the 

cloud-cleared results for the 800-816 cm-1 long-wave water vapor absorption region, 

the 1025–1041 cm-1 ozone absorption region, and the 1284–1300 cm-1 shortwave water vapor region. 
190 

Figure 239. Cloud-clearing results for July 6-7, 2010. The upper left panel shows the reference 

window region and cloud flag while the upper right, lower left, and lower right hand panels show the 

cloud-cleared results for the 800–816 cm-1 long-wave water vapor absorption region, the 

1025–1041 cm-1 ozone absorption region, and the 1284–1300 cm-1 shortwave water vapor region. 

5.3.7.2 Cloud Correcting 

As defined in the previous section, the radiance measured by a spectrometer whose field of view 

contains a broken and/or semi-transparent cloud was given in Equation 5.3.3. The cloud amount 

can then be solved from the radiance observations for two different spectral regions, ν and w. The 

solution is 

(5.3.6)

And 

(5.3.7)

In Equation 5.3.7 the wavenumber w is the channel window region and the wavenumber ν refers 

to the semi-transparent spectra channel. The clear window radiance, RCLR(w) is known from either 

surrounding time cloud-cleared window radiance observations or estimated from a clear sky 

radiative transfer calculation based on forecast model atmospheric profiles, and RCLD is the 

radiance calculated from the forecast model atmospheric profiles for an opaque cloud placed at 
191 

various distances from the instrument, d. Because the clear sky window radiance is very small 

(near zero from aircraft or high altitude observatory levels) compared to the spectral atmospheric 

radiances used for atmospheric turbulence detection, the result is not very sensitive to the 

accuracy of the assumed clear window radiance, RCLR(w). For the Hyper-Cam, operating at 

16 cm-1 spectral resolution, w = 1075–1091 cm-1 and ν = 1251–1283cm-1. The second channel is 

mainly sensitive to methane, which is a fairly uniformly mixed gas, so that the ratio of the 

calculated cloud amount in this band relative to the window region should be proportional to the 

distance of the observed cloud from the instrument (i.e. the difference between the observed 

cloud radiance and the clear radiance in the methane channel depends upon the optical depth of 

methane between the cloud and the instrument). It is noted that in the calculation of N, the clear 

radiance in the numerator is taken as the pre-derived clear air radiance obtained by the N* method 

described in the previous Cloud Clearing section. However, since the cloud radiance is calculated 

using model forecast atmospheric profiles, the clear air radiance for the denominator of the 

equation is specified using a clear sky radiance calculation based on the same model profiles used 

to calculate the cloud radiance. This is important to alleviate any systematic discrepancies 

between observed and calculated radiance due to radiative transfer model or atmospheric state 

errors. 

The cloud distance and effective cloud amount are calculated using: 

(5.3.8)

Here, both the clear air and the cloudy radiance are obtained from radiative transfer calculations 

so that their difference becomes zero for cloud distances which cannot be seen by any particular 

spectral channel due to strong atmospheric absorption. 

Figure 240 shows an example result for a very cloudy condition observed from ground-based 

ASSIST measurements during the Chemistry and Physics Atmospheric Boundary Layer 

Experiment (CAPABLE) conducted during the June – August 2010 time period. The ASSIST 

data were transformed to the 16 cm-1 spectral resolution of the Hyper-Cam. As can be seen from 

the upper left-hand panel, clouds existed throughout much of the day, with the heaviest cloud 

occurring during the first half of the day. 
192 

Figure 240. Cloud-correction results for June 22, 2010. The upper left panel shows the reference 

window region and cloud flag while the upper right and lower left show the cloud-cleared and 

cloud-corrected results for the 1251–1283 cm-1 methane and 1210–1216 cm-1 water vapor absorption 

regions, respectively. The derived distance to cloud is shown in the lower right hand panel. 

5.3.7.3 Cloud Detecting 

Data from the MRS November 17, 2011, Run 4 dataset was used to illustrate the development of 

the Cloud Detecting Algorithm. Detailed results of the temporal variability analysis for this 

dataset are given in the following section. The cloudy area brightness temperature was estimated 

to be about 240 K. We performed computations for cloud brightness temperatures from 215 K to 

250 K with 5 K increments to see how this threshold value affects the temporal variability 

pictures. A cloud mask was applied in such a way that all pixels with radiance values higher than 

R (TCLD = 215, 250, 5 K) have been excluded from the consideration, which leads to a different 

number of pixels averaged over 1007 data cubes total. For example, as can be seen in Figure 241, 

the upper and bottom thirds of the whole frame are always excluded, while for the central 1/3 of 

the frame the number of averaged pixels is changing from 1007 (always clear) to 0 (always 

cloudy) from left to right. The number of averaged pixels depends also on the given value of 

threshold cloud temperature. 

The temporal variability results, with cloud contributions over a threshold value (TCLD = 240 K) 

eliminated, are presented in Figure 241. Eight channels with central wavenumbers from 863 cm-1
193 

to 1115 cm-1 are shown in brightness temperature units, with the same color scale 0 – 4 K for 

each panel. 

Figure 241. The temporal variability of the scene when cloudy pixels TCLD > 240 K are eliminated. 

Unit is brightness temperature, the same color scale 0–4 K is used for all panels. 

The temporal variability when TCLD varies from 215 K to 250 K is presented in Figure 242 for the 

transparent channel #8. One can note that even as low as 215 K there is cloud signal. For 

comparison, Figure 243 shows temporal variability for the opaque channel #18 

(O3 band, ~1035 cm-1). The panels shown are for threshold values of TCLD = 260 K, 265 K, and 

270 K. There is no signal for TCLD ≤ 255 K in this channel. Note also that the color scale is a 

factor of 2 smaller (0 – 2 K) in Figure 243 than in previous figures. 
194 

Figure 242. The temporal variability of the scene for the transparent channel #8 (~903 cm-1) when 

different cloud brightness temperature threshold values are applied (215 K to 250K, increment 5 K); 

November 17, Run 4. 

Figure 243. The temporal variability of the scene for the opaque channel #18 (~1035 cm-1) when 

different cloud brightness temperature threshold values are applied (260, 265, 270 K); November 17, 

Run 4. Note that color scale is a factor of two smaller (0 – 2 K) than the previous figures. There are 

no pixels for TCLD ≤ 255 K. 

A second method was developed to exclude cloudy pixels based on comparison of sky radiance 

and cloud radiance as shown in Figure 244; the spectra shown are from the same row of pixels so 

that the elevation angle for both would be the same and not create a difference in magnitude. 

These spectra are at 16 cm-1 resolution, so the narrow spectral lines characteristic of a sky 

spectrum cannot be seen in the measured sky radiance; however, there is enough difference in the 
195 

two spectra so that a determination of sky or “other” can be made for every pixel in the scene. 

The cloud spectrum exhibits much higher radiance than the sky spectrum; additionally it has 

fewer spectral features and follows more closely the trend of a blackbody. 

Figure 244. The radiance from a sky pixel compared to the radiance from a cloud pixel. 

In order to determine the areas of clear sky, the following procedure was followed. Channel #8 

(903.02 cm-1) from each data cube in time was chosen to be the frame that exhibited a substantial 

difference between sky and cloud spectra. Channel #35 (1261.67 cm-1) from each data cube in 

time was chosen to be the frame that exhibited the most similarity between sky and cloud spectra. 

For every time in the series, channel #35 was subtracted from channel #8. A binary image was 

created for every time step, whereby if the result of the subtraction was positive, a one was 

assigned to the pixel, and if the result of the subtraction were negative, a zero was assigned to the 

pixel. By overlaying these binary images over the difference images described in the previous 

section, everything but clear sky should be masked, which aids in the determination of whether 

variability can be attributed to temporal changes in cloud position. Variability images from the 

beginning and end of the time series, both unmasked and masked, are shown in Figure 245. 
196 

Figure 245. The variance in radiance at the beginning of the thirty minute run (top two images) vs. 

the variance in radiance at the end of the thirty minute run (bottom two images). The images on the 

right have been masked by the cloud removal test. 

This simple algorithm to test for sky also masks clear sky at the horizon as the atmosphere here is 

more opaque and its radiance looks more like a blackbody. Therefore, a second test was 

developed that would compare the relative change in radiance between channel #8 (903.02 cm-1) 

and channel #9 (916.30 cm-1). As shown in Figure 246, both sky spectra, regardless of elevation 

angle, exhibit a much greater change between the two wavenumbers than the cloud spectrum. 

Figure 246. The radiance from sky pixels at two elevations compared to the radiance of a cloud pixel. 
197 

The relative difference of the local radiance between 903.02 cm-1 and 916.30 cm-1 of the three 

spectra in Figure 246 is 8.133 x 10-4 W/m2

sr in the case of the sky at the high elevation, 

2.410 x 10-4 W/m2

sr in the case of the cloud, and 7.681 x 10-4 W/m2sr in the case of the sky at the 

horizon. Because the sky radiance changes approximately three times faster in the case of both 

sky spectra, this derivative is checked in addition to the radiance difference between 903.02 cm-1

and 1261.67 cm-1. Variability images from the beginning and end of the time series, both 

unmasked and masked with the new constraint are shown in Figure 247. 

Figure 247. The variance in radiance at the beginning of the thirty minute run (top two images) vs. 

the variance in radiance at the end of the thirty minute run (bottom two images). The images on the 

right have been masked by the cloud removal test as in Figure 245, but with the additional constraint 

of checking the change in radiance between 903.02 cm-1 and 916.30 cm-1. 

5.3.8 Temporal Variability Analysis Results 

Hyper-Cam calibrated data for November 16, 2011, Run 1 (cubes 1917 to 2534, 618 total) with 

16 cm-1 spectral resolution were processed first, following the methodology outlined in the 

Temporal Variability Analysis Theory Section. After generating a bad pixel map for this run by 

excluding pixels for which calibrated radiances in all channels were larger than 0.04 W/m2

cm-1sr, 

about 2000 pixels were classified as bad out of 320*256 = 81920 total. Again, horizontal mean 

radiances were obtained by row averaging (excluding pixels marked as bad). These mean 

radiances were then subtracted from the measured radiance, separately for each spectral channel 

and data cube. After that, the temporal variability of the signal was computed as the standard 

deviation of the resulting radiance differences by averaging over all 618 cubes. The last step was 
198 

done separately for each spectral channel and image pixel. Finally, the data cube of this temporal 

variability, with dimensions 320 x 256 x 618, was analyzed. 

To estimate which channel exhibited the highest temporal variability, averaging over all image 

pixels for a given channel was performed. Figure 248 shows the spectral dependence of these 

statistics on the corresponding mean, minimum, and maximum values. Note that the first and last 

three channels have been excluded from consideration since they obviously suffer from the 

wrap-around effect. From Figure 248, channels #4 and #5 (~850 cm-1 and 863 cm-1) have been 

selected for display as these channels show the highest temporal variability. The corresponding 

full-size images of temporal variability for these two channels are shown in Figure 249. 

Figure 248. Statistics for temporal variability of the signal obtained by averaging over all image 

pixels for each channel. Blue arrows indicate the two channels shown in the full-size images in Figure 

249. 

The temporal variability is relatively large; the full-scale range shown in Figure 249 is from 

2x10-4 to 2x10-3 W/m2

cm-1sr. The relatively high variability in the upper left-hand corner of the 

image is due to the lost calibration amoeba pattern discussed previously. The red spots in the 

lower left corner are due to a tree in the field of view, where an arbitrary high variability value 

was assigned. On the upper right-hand side of the tree, there are two areas where the temporal 

variability is comparatively high. These areas were later confirmed to be due to clouds. 

Figure 249. Full-size image of temporal variability for channels #4 (left panel) and #5 (right panel)

that have maximum values of the signal. 
199 

Images were then created for each channel for every data cube in sequence. An example of three 

images from the fourth channel is shown in Figure 250. Note that all three images have the same 

color scale, and different features appear in the images as the sequence continues. Unfortunately 

there are no webcam images for this run, so it is unknown whether or not the features seen in 

Figure 250 are clouds. 

Figure 250. Radiance (W/m2sr) imagery at 850 cm-1 from November 16, 2011, Run 1. Each frame 

(from top to bottom) was recorded at 18:13:45, 18:23:03, and 18:26:37 UTC, respectively. 

This data set lacked not only webcam imagery to verify or exclude the existence of clouds, but 

also D&P data and pilot reports of turbulence in the area. Therefore, going forward, we decided 

to focus on several runs from November 17, 2011. On this day, we could see lenticular clouds; 

also, there were several PIREPS of turbulence, and we have complete data, including 

simultaneous Hyper-Cam, webcam, and D&P datasets. 

Six processed runs from November 16 and 17 contain 1000+ data cubes each, so the temporal 

interval is about twice as long as November 16 Run 1 (618 cubes). The computation of temporal 

variability ends up with one cube of data that is the variability (i.e. standard deviation of the 

radiance) for each pixel/channel separately. 

Averaging this resulting cube over all pixels for a given channel helps to determine which 

channels have the highest sensitivity to turbulence. Figure 251 shows this spectral dependence for 

all 7 runs processed, from this we again conclude that channel #4 (~850 cm-1) shows the most 
200 

variability. Corresponding images of temporal variability at this channel for all runs in radiance 

units are shown in Figure 252. Note that while making the images in Figure 252, the bad pixel 

values have been replaced either with the average over good pixels in ± 2 surrounding area or 

with a zero value when an insufficient number of valid radiances are observed. Also note that the 

“amoeba” pattern is present in this data. 

Figure 251. Spectral dependence of the temporal variability of the scene obtained by averaging over 

all image pixels for given channel, 7 runs. 

Figure 252. Image of temporal variability (radiance units) for channel #4, that have maximum values 

of the signal, 6 runs. 

November 17, Run 4 data was the next run analyzed and was chosen because the field of regard 

was positioned directly under a lenticular cloud. During this time series, clouds were crossing the 

instrument FOV, which resulted in significantly larger disturbances of the measured signal than 

wavenumber, cm-1

900 1000 1100 1200 1300

Temporal Variability, Rad units

2.0e-4

4.0e-4

6.0e-4

8.0e-4

1.0e-3

1.2e-3 Nov16, run1

Nov16, run2

Nov16, run3

Nov16, run4

Nov16, run6

Nov17, run7

Nov17, run8
201 

was observed in all runs processed before. This disturbance is clearly seen in Figure 253, which 

shows the spectral dependence of temporal variability of the signal in radiance units for all eight 

runs processed to the point of the November 17, Run 4 analysis. Averaging the standard deviation 

over all pixels, separately for each spectral channel, provides the spectral dependence of the 

computed temporal variability. Figure 254 shows the same quantity in brightness temperature 

units for November 17, Run 4 only. Corresponding images of temporal variability for channels 4 

and 23 are shown in Figure 255 and Figure 256. 

Figure 253. Spectral dependence of the temporal variability of the scene obtained by averaging over 

all image pixels for a given channel, 8 runs total, radiance units. 

Figure 254. Spectral dependence of the temporal variability of the scene obtained by averaging over 

all image pixels for given channel, November 17, Run 4, brightness temperature units. 

wavenumber, cm-1

900 1000 1100 1200 1300

Temporal Variability, Rad units 0.0005

0.0010

0.0015

0.0020

0.0025

Nov16, run1

Nov16, run2

Nov16, run3

Nov16, run4

Nov16, run6

Nov17, run7

Nov17, run8

Nov17, run4

wavenumber, cm-1

900 1000 1100 1200 1300

Temporal Variability, BT units [K]

0.5

1.0

1.5

2.0

2.5

3.0
202 

Figure 255. Image of temporal variability for channel 4, radiance units (left panel) and brightness 

temperature units (right panel), November 17, Run 4.

Figure 256. Image of temporal variability for channel #23 in radiance units (left panel) and 

brightness temperature units (right panel), November 17, run4. 

The most visible patterns seen in the previous figures are caused by cloud motion across the FOV 

during time series of measurements; the clouds can be seen in the radiance imagery shown in 

Figure 257. 

80 64 48 32 16

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)

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203 

Figure 257. From left to right: channel #5 from the first, middle, and last cubes of data, November 

17, run 4. 

Despite cloud cover, some interesting features were observed on the edges and in gaps between 

the clouds. The temporal variability had been computed over all good pixels, including cloudy 

ones. Therefore, the “Cloud Detecting” methodology for excluding cloudy pixels from the 

statistical averaging described in the previous section was implemented. 

Once the algorithms were implemented and upon closer inspection, the variability seen appears to 

be due to a thick haze at the horizon. When the haze at the horizon is compared with a clear day, 

it seems to be thicker and could be leading to the variability; the radiance of the sky for a clear vs. 

the cloudy day is shown in Figure 258. The box in the left-hand image is roughly the same FOV 

as the image on the right. The haze in the radiance images and the cloud removal analysis 

performed lead us to believe that the variability in the imagery was not due to mountain waves. 

Figure 258. LWIR imagery from November 16 (a clear day) vs. November 17 (a cloudy day). 

Radiance in W/m2

sr, at 926.6 cm-1. 

It was determined that November 17, Run 4 data were too cloudy to discern the source of any 

variability in the scene. Thus, the eighth dataset from November 17 was investigated for any 

signs of variability next. This dataset was chosen for the following reasons: 

1. The webcam imagery from that time frame indicated minimal cloud coverage. 
204 

2. There were four PIREPS of turbulence in the vicinity of MRS: three moderate and one 

light. Two of these moderate reports identified the type of turbulence as mountain wave. 

The other two reports did not indicate the type. 

The data from Run 8 did not show any evidence of turbulence as indicated by variability in the 

scene. Therefore, two other analyses were performed on these data: gas detection and anomaly 

detection. These methodologies are described in the following two sections. 

5.3.9 Gas Detection Algorithm Analysis 

Temperature and moisture variations are associated with mountain waves. In Year 2 of this 

program, hyperspectral gas detection algorithms were used in an attempt to detect wake vortices 

based upon the exhaust gases they entrain [20]. The Adaptive Cosine/Coherence Estimator (ACE) 

had proven to be effective and was thus used to search for water vapor in the MRS dataset. A 

water vapor spectrum was defined as the target under search. 

Radiosonde data from November 18, 00 Z was the closest in time to the Run 8 dataset (beginning 

at 22:12 UTC, November 17). The water vapor target was modeled at two different altitudes; 

these were chosen based on radiosondes only and not on the altitude of any PIREPS. The 

underlying assumption here is that if turbulence PIREPS exist in the general vicinity of MRS then 

turbulence should exist in our dataset. The Denver radiosonde temperature, relative humidity, and 

mixing ratio data from November 18, 00Z, are shown in Figure 259. 

Figure 259. Denver, CO radiosonde data (November 18 2011 00Z). The dry slot is circled in red. Note 

the different scale on the altitude axis of the temperature plot. 
205 

A dry slot can be seen in the relative humidity and mixing ratio data that exist at an altitude 

between 5141 and 5761 meters. The temperature and pressure were extracted for these altitudes, 

and then the ACE detector was run on all 1,019 data cubes from Run 8 twice, once with each of 

the modeled water vapor spectra. The two sets of ACE results for water vapor at each altitude 

were investigated for possible differences by subtracting the results of the two sets; however, 

there was no indication of any turbulent event using the algorithms and parameters we tested. 

Because of small signals (i.e. water vapor at low temperatures) and atmospheric composition (i.e. 

searching along a long atmospheric path full of water vapor), the detection of temporal anomalies 

could be of more use than the ACE detector as a preliminary step. If anomalies are detected, they 

could be investigated further by then looking at the spectra of the pixels that show anomalous 

behavior. Two temporal anomaly detectors were investigated and are described in the following 

section. 

5.3.10 Temporal Anomaly Detection Analysis 

Temporal anomaly detectors based upon the algorithms developed by Heinz [32] were 

implemented on the MRS data. The two algorithms are the TSAD (Temporal-Spectral Anomaly 

Detector) and the TSCD (Temporal-Spectral Covariance Detector). The TSAD is a ratio of 

anomalies at each time, while the TSCD is a ratio of the covariances at each time. Thus, a TSCD 

value of about 1 corresponds to the negligible temporal changes in the scene; otherwise, the 

temporal anomalies may be detected. One of the advantages of the TSCD analysis technique is 

that it gathers information over the entire measured spectrum. 

The spectral data must first be de-meaned, and it should be noted that the global mean is not used. 

Instead, means are calculated independently from horizontal strips of sky due to varying sky 

radiance with viewing elevation angle (which was implemented during the wake vortex part of 

this program). This mean calculation was the only change made to Heinz’s algorithms, which 

were then run on data cubes from Run 8 from November 17. A median filter was applied to all 

results to accentuate groups of highly scoring pixels while eliminating those with no neighbors. 

When the TSAD and TSCD results were made into a movie, a flashing blob appeared near the 

bottom center of the frame. This corresponds to the approximate center of the entire frame, since 

the bottom was cut out of analysis (it contained trees). The flashing is not regular in frequency, 

but occurs every 1, 2, 3, or 4 frames; each frame is separated by 2 seconds in real time. The 

TSAD showed a similar result. To check if this phenomenon was perhaps an artifact of the Telops 

measurement, the TSAD and TSCD algorithms were run on another clear dataset from a different 

day. The dataset chosen was Run 5 from November 18. The artifact is apparent in the Run 5 

analysis as well; therefore, we believe it may be an artifact of the measurement and the data might 

require more pre-processing or different analysis to identify temporal anomalies. 

For confirmation of the blinking phenomena observed in the TSCD results, the algorithm was 

also implemented in a comparison of this algorithm to the temporal variability analysis described 

in previous sections. Figure 260 shows cross-data cube TSCD images for the first 15 cubes 

(from 14076 with 14075 to 14090 with 14089), November 17, Run 8. One may note changes in 

TSCD coefficients for some moments (for example, left center and right bottom panels). We have 

already reported temporal variability for this run obtained by averaging over all cubes (about 30 
206 

min of observations), which is shown in Figure 252 and again in Figure 261 for the November 17, 

Run 8 run only. In this image, the 100 x 100 pixel red square marks the area of interest for the 

TSCD analysis. 

Figure 260. TSCD images for the first 15 data cubes, November 17, Run 8. 

Figure 261. Temporal variability for November 17, Run 8 over all data cubes (~30 min), channel #4. 

The area of interest is marked with a red square. 

Note that in addition to the window size, the temporal variability image in Figure 261 is for an 

entire 30 minute dataset while the TSCD analysis results shown in Figure 260 were only 

computed for 2 minutes of observations; also the results shown in Figure 261 were computed for 

a 4 x 4 pixel average while the results shown in Figure 260 have no spatial averaging. Therefore, 
207 

for a direct comparison of the TSCD and the temporal variability methods, we also redid the 

temporal variability analysis, but for the first 70 data cubes only and with no spatial averaging. 

The resulting image for the same channel 4, but for 70 data cubes only, is shown in Figure 262. 

Figure 262. Temporal variability for November 17, Run 8 over first 70 data cubes (~2 min), channel 

#4. 

Red dots are associated with bad pixels (i.e. low responsivity detector elements). The horizontal 

lines, seen in Figure 262, and also noticeable in Figure 260, are aircraft contrails, as verified by 

the visual camera images taken during this run. Comparing Figure 261 and Figure 262 indicates 

that the overall period of time averaged is critical for temporal variability estimates. Also, the 

TCSD technique yields results similar to those obtained with the original elevation angle 

normalized pixel radiance temporal standard deviation technique. 

The individual frames from November 17, Run 8, did show one interesting result in both the 

TSAD and TSCD analysis. This anomaly occurred at frame 332 in the Run 8 series, which 

corresponds to data cubes 14408 and 14409. These images are shown in Figure 263 and 

Figure 264. 

Figure 263. TSAD Result, November 17 Run8, frame 332 in time series. 
208 

Figure 264. TSCD result, November 17 Run 8, frame 332 in time series. 

It is unknown to what the circles correspond. There have been other data in which birds passing 

through the field of view have been identified, but they appear as streaks. The radiance data at 

several wavenumbers from this time are shown in Figure 265. 

Figure 265. Radiance (W/m2sr) at several wavenumbers from data cube 14408. 
209 

The radiance of the spots, at most wavenumbers, is less than the radiance of the rest of the sky at 

that altitude. For this reason, the fact that they are regularly spaced, and by comparison with the 

visible webcam movies, we do not believe they are clouds. The spots are not evident at any other 

wavenumbers. 

Attention then shifted to MRS data from November 17, Run 5, because it was relatively cloud 

free and there were PIREPs in the vicinity during the run. This was also the run for which a WRF 

model was created. A band-averaged radiance image from the first datacube is shown in Figure 

266; this figure shows that there is clear sky in approximately the upper 100 rows of the image. 

This region of sky stayed relatively clear for the entire 30 minute run. The analysis that follows 

was done on these top 100 rows. 

Figure 266. Average radiance of first datacube in Novenber 17, Run 5 set, units W/m2cm-1sr.

Run 5 consists of 1037 Hyper-Cam datacubes; over 20,000 images from this dataset were 

processed. The radiance at several bands is shown below in Figure 267 for “Datacube 100” in the 

time series and in Figure 268 for “Datacube 898” in the time series. “Datacube 100” represents 

results from a randomly selected time that is typical of most of the results. “Datacube 898” 

represents results of a time that showed anomalous behavior. In these data, the anomalies to 

which we refer are represented by areas of similarly colored pixels. For a uniform background, 

such as sky, we would expect the distribution of color to be fairly uniform in azimuth with some 

random variation for noise in the sensor. Therefore, pixels of similar values that are clustered, or 

not randomly distributed in the spatial dimension, could be indicative of an anomaly. These 

clusters, or “blobs”, appear in the variability analysis presented in Figure 270 and Figure 274. 

However; note that all images are scaled into 256 color bins, so even narrow distributions in pixel 

values in an image will show up as different colors. 
210 

Figure 267. Radiance images at: Top: Frame 12 (956.14 cm-1) and Frame 17 (1022.53 cm-1), Middle: 

Frame 15 (995.97 cm-1) and Frame 31 (1208.45 cm-1), and Bottom: Frame 21 (1075.65 cm-1) and 

Frame 22 (1088.93 cm-1). All at time step 100. Radiance units W/m2

cm-1sr. 

Figure 268. Same as Figure 267, but at time step 898. 

The radiance imagery does not show anything very interesting at either time step. Next, we show 

the difference from the mean for all times for each respective spectral waveband. That is, the 

mean was calculated at each pixel (x, y) for all t; the results are shown at t = 100 (Figure 269) and 

t = 898 (Figure 270). 
211 

Figure 269. Difference from the mean at: Top: Frame 12 (956.14 cm-1) and Frame 17 (1022.53 cm1), Middle: Frame 15 (995.97 cm-1) and Frame 31 (1208.45 cm-1), and Bottom: Frame 21 (1075.65 

cm-1) and Frame 22 (1088.93 cm-1). All at time step 100. Radiance units W/m2

cm-1sr. 

Figure 270. Same as Figure 269, but at time step 898. 

Figure 269 and Figure 270 give representations of the time variability at several spectral 

wavebands in the time series. While no evidence of anomalous behavior is evident at time step 

100 (Figure 269), we can begin to see anomalies at time step 898. 

The following two figures show simple band ratios at t = 100 (Figure 271) and t = 898 (Figure 

272). The top, middle, and bottom panels correspond to the ratios of the radiance images in the 

top, middle, and bottom panels of Figure 267 and Figure 268, respectively. 
212 

Figure 271. Ratio of radiances for bands: Top: Frame 12 (956.14 cm-1) and Frame 17 (1022.53 cm-1), 

Middle: Frame 15 (995.97 cm-1) and Frame 31 (1208.45 cm-1), and Bottom: Frame 21 (1075.65 cm-1) 

and Frame 22 (1088.93 cm-1). All at time step 100. Radiance units W/m2cm-1sr. 

Figure 272. Same as Figure 271, but for time step 898. 

Finally, the following two figures show the difference from the mean of the band ratios at t = 100 

(Figure 273) and t = 898 (Figure 274). 
213 

Figure 273. Difference from mean of the band ratios shown in Figure 271. 

Figure 274. Difference from mean of the band ratios shown in Figure 272. 

While the figures shown above indicate there is something anomalous at time step 898, the 

magnitude of the variability does not appear to be significant. These anomalies could be an 

artifact of a bad calibration or corrupted data and do not necessarily indicate the presence of 

turbulence. These anomalies do not appear at time steps 897 or 899. 

Note that we do not believe the blobs that show up in the analysis are related to the amoeba 

patterns we have described in previous reports. The amoeba patterns are apparent in uncalibrated 

data because they represent the raw response on the detector. An interferogram image from the 

datacube at time step 898 is shown in Figure 275; note that the raw signal “amoebas” do not seem 

occur at the same locations as the anomaly “blobs.” 
214 

Figure 275. Interferogram image from the datacube at time step 898. The image is a “slice” of the 

interferogram at OPD position 100 (near center burst). The color scale is in raw intensity counts. 

5.4 Mountain Research Station Data: Simulated vs. Observed 

Radiance signals due to mountain lee wave turbulence were simulated for comparison with actual 

measurements obtained during the MRS field experiment in November 2011. In addition, the 

viewing geometry and spectral resolution of the simulations were chosen to match as closely as 

possible to the actual conditions of the measurements. The simulated radiances were processed 

the same way as measured ones, i.e. temporal variability of the signal was estimated. 

5.4.1 LBLRTM Simulation Using Denver Radiosonde Data 

Fourteen data cubes from the November 17, 2011 measurements were analyzed. That day of 

observation was clear and very windy, and pilot reports revealed turbulent conditions during the 

observation time. The analysis had two main goals: 

1. To compare observed data with simulated radiances; 

2. To visualize observed data after processing, aiming for evidence of the mountain lee 

wave turbulence. 

The 14 Hyper-Cam data cubes each consist of 256 vertical by 320 horizontal spatial pixels, for 

each of which the radiance had been measured in 41 channels from ~810 cm-1 to ~1341 cm-1 with 

16 cm-1 spectral resolution. The first and last three channels were excluded from the analysis 

since they suffer from rapid fall-off of detector responsivity. The bottom 96 rows of each image 

were excluded from the analysis too, because nearby trees were in the instrument’s field of view 

(FOV). Thus, the processed data cubes contain a total of 160 x 320 x 35 x 14 radiance spectra. 

The total time period of processed observations was about one minute. 

The angular size of the FOV was assumed to be 20° x 25° in vertical/horizontal directions, 

respectively, which corresponds to approximately 0.078° angular size for the image pixels. The 

bottom row had an elevation angle 0° above the horizon. Figure 276 shows the observation 

geometry as FOV size in vertical/horizontal projections as a function of the distance from the 

point of observation. The MRS altitude is ~9,500 feet. 

In order to validate the radiometric calibration of the observed data, comparisons were made with 

LBLRTM simulations. The closest radiosonde temperature and water vapor profiles (Denver) for 

November 17, 12Z, and November 18, 00Z, were averaged to approximate the observation time 

of about 1 PM MST. Concentrations of other gases needed for the radiative calculations were 
215 

taken from the US-1976 standard atmosphere, excepting CO2, which was set to 385 ppm. The 

resulting radiosonde temperature and water vapor profiles used in the simulations are shown in 

Figure 277. LBLRTM simulations were performed for ten elevation angles ranging from ~8° to 

19.5° with an increment of ~1.25°, which correspond to Hyper-Cam measurement rows 97–256. 

For the comparison of observed and calculated radiances, 16 consecutive rows from the actual 

observations are merged into a single bin, starting from row 97 (i.e. exactly the piece of data 

picked for analysis). The ten radiances simulated using LBLRTM were compared with averages 

over the same 16-row-wide-bin observed radiances. 

Figure 276. The Hyper-Cam observation geometry, November 17, 2011. 

Figure 277. The atmospheric state, averaged over the two closest times, from the closest radiosonde 

launch site (Denver). 

The result of this comparison (together with variability of observed data over the corresponding 

means at 16 row-bins) is presented in Figure 278. One can note the relative agreement in the 

spectral shape of the observed and simulated radiances, although there are many regions 

Telops FOV,

Vertical cross-section (=20o

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(e.g. 960 cm-1, 1020 cm-1, 1105 cm-1, 1260 cm-1, and 1300 cm-1) where there is considerable 

disagreement in the spectral variation of the observed and calculated radiance spectra. Also, there 

is considerable disagreement in the absolute magnitude of the radiances with the observed 

radiances being generally higher than the simulated radiances, except in the water vapor 

absorption region near 1300 cm-1. These systematic differences may be due to a combination of 

absolute calibration uncertainty and the use of an average Denver radiosonde to represent the 

atmospheric state at the time of the observations. The disagreement in absolute values for the O3

band (~1050 cm-1 centered) is explained by using the US-1976 standard ozone profile, since no 

in-situ observations of ozone were available. 

Figure 278. Comparison of the mean observed (black), LBLRTM-simulated (red), and the standard 

deviation (green) of the observed radiances around their means for ten elevation angles. 

5.4.2 FLI-FM Simulation with March 6, 2004 WRF Model 

For the first comparison, highly spatial and temporal resolved data were not available; therefore, 

atmospheric states obtained with a WRF model for a lee wave turbulent event on March 6, 2004 

(described in Section 5.1.4) were used. As with previous simulations, the arbitrary “center” of 

turbulence was assumed to be located approximately at 39.5°N, 105.0°W. Because of lack of time 

resolution, temporal variability of the signal was created by changing the observer point of view 

(i.e. different parts of WRF data cube are then observed for each observer location). For the 

simulations, the observer altitude was equal to 2.75 km (~MRS altitude), the observer latitude 

was fixed at 39.5°N, and the observer longitude was variable in range from 105.5075°W to 

105.0°W with a 0.07025° increment. Thus, simulations were performed for eight locations total, 

with the distance to the “center” of turbulence varying from ~45 km to 0 km with an increment of 

about 6.5 km. However, because lee waves are atmospheric standing waves, it is hard to define 

the time increment that corresponds to the observer position increment used. 

The instrument field of view was assumed to be 5° in both horizontal and vertical directions, with 

15 x 15 pixels. The central pixel for all locations had an elevation angle of 13.5° and its azimuthal 

direction was directly to the east, towards the turbulence center. Thus, 15 x 15 x 8 = 1800 spectra 

total were computed and combined into eight data cubes, similar to Hyper-Cam output. The 35 

wavenumber, cm-1

800 900 1000 1100 1200 1300

0.00

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Mean Observed

LBLRTM

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217 

channel radiances for a Hyper-Cam 16 cm-1 resolution datacube were obtained by boxcar 

averaging the FLI-FM2 high resolution unapodized spectra in the range ~800-1300 cm-1. The 

three first and three last Hyper-Cam channels (there are 41 total) are not considered here because 

it was found earlier that they suffer from a wrap-around effect of the spectral convolution. 

Figure 279 and Figure 280 show the mean radiance and its standard deviation for the central pixel 

(8, 8) of the image, obtained by averaging over eight different observer locations (considered to 

be equivalent to eight time moments) in radiance and brightness temperature units. 

Figure 279. Mean radiance and its standard deviation for the central image pixel, obtained by 

averaging over 8 observer locations. W/m2/ster/cm-1

0.000

0.005

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218 

Figure 280. The same as Figure 279, but in brightness temperature units. 

The highest signal and variability are observed in the ozone band 1000–1080 cm-1 and water 

channels ν > 1250 cm-1. While high variation in water channels is expected and not surprising, the 

variations at the ozone band need some additional comments. Since ozone is a fixed gas in 

FLI-FM2, variations there can be explained by atmospheric temperature variations only. Thus, 

simulations show significant sensitivity (i.e. a standard deviation greater than the instrument 

noise: 0.16 K BTD at 300K, see Appendix A) to temperature and water vapor turbulence. 

Figure 281 presents the spectral temporal variability of simulated radiances in comparison with 

those obtained from actual measurements. The temporal variability was estimated the same way 

for simulated and measured data (i.e. by averaging over all image pixels and observer locations, 

in order to define the most sensitive channels). In general, the simulated radiances display less 

variability than the measured ones. This is expected since the simulated radiances are free of 

measurement noise and cloud contamination. As can be seen, the spectral features of the 

atmospheric turbulence are masked by the instrument and cloud noise, except possibly for the 

water channels ν > 1250 cm-1. The masking of turbulence signals by instrument and cloud noise is 

most noticeable for the ozone absorption band (1000–1100 cm-1). The fact that November 16, 

Run 1 (black curve) has a much larger temporal variability than the other runs shown, except for 

in the central region of the ozone absorption band, is probably the result of larger cloud 

contamination in this run than for in the others runs shown in Figure 281. The enhanced 

absorption by ozone in the path between the instrument and the cloud reduces the instrument’s 

sensitivity to cloud variability in this spectral region, causing the decrease in radiance variability 

within this band, as shown in Figure 281. 

K

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280

Mean

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K

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219 

Figure 281. The temporal variability of simulated radiances (dark red curve with symbols) in 

comparison with that obtained from observations of several runs. Red arrows indicate channels for 

which full-size images of temporal variability are shown on the next figure. 

Figure 282 presents full-size images of temporal variability of the simulated radiances for the 

channels marked with red arrows in Figure 281, which show the highest signal in the brightness 

temperature domain. The signal is in the 0.5–0.8 K range, as obtained from simulations. 

Figure 282. Full-size images of the signal temporal variability, obtained from simulations (brightness 

temperature units, K). Note that color scale is slightly different for different channels. 

wavenumber, cm-1

900 1000 1100 1200 1300

Temporal Variability, W/m2/ster/cm-1

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Simulated

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Ch 21 Ch 32 Ch 34 Ch 38

Ch 5 Ch 6 Ch 8 Ch 19
220 

5.4.3 FLI-FM2 Simulations with November 17, 2011 WRF Model 

Because the simulations described in the previous sections were modeled from either incomplete 

or incorrect meteorological data, it was determined it would be worthwhile to create a WRF 

model for the exact time and location of the MRS test for comparison with the measured HyperCam data. November 17, 2011 was a day in which lenticular clouds were present and PIREPs 

documented mountain wave turbulence. The optimum time and location of the WRF model were 

identified by the following criteria: 

1. Relatively cloud-free sky in Telops Hyper-Cam field of view for measurements recorded 

during that time. 

2. Turbulence PIREPs reported in general vicinity of viewing direction during time of run. 

Three runs on November 17 were cloud free; the PIREPS during these three runs and field of 

view of the sensor were plotted in Google Earth. Figure 283 below shows a view of Colorado 

with the MRS test observation point and PIREPs that were reported during the time of each of the 

three runs. Only the FOV of the Hyper-Cam during Run 5 is shown on this plot because it was the 

only run in which the sensor was aiming in the direction of a PIREP. 

Figure 283. Google Earth view of Colorado with PIREPs (yellow markers) and Hyper-Cam FOV (red 

lines) plotted.

5.4.3.1 New WRF Model: November 17, 2011 

The WRF configuration was identical to that described in Frehlich et al [33]. An example set of 

figures showing various atmospheric parameters at 20:00 UTC the day of the data collection is 

displayed in the following charts. 
221 

To understand how the modeled data are represented spatially, consider only the east-west red 

band drawn in Figure 285 and its associated figures (Figure 286 to Figure 292). The red line, 

drawn at an altitude of 12 km, forms an upper altitude bound on the vertical plane running eastwest as the line shows. It is this ‘window’ or plane that is depicted in Figure 286 through Figure 

292. Each of those charts contains modeled data up to 12 km ASL along the east-west path, with 

the MRS test site roughly in the middle of the east-west (and later, north-south) red band. In each 

‘ground-level’ plot showing the Rocky Mountains in profile, the MRS site is marked with a blue 

cross. The elevation angles of the Hyper-Cam are likewise marked by blue lines. An important 

note is that the FOV azimuthal angles are not depicted in the modeled data charts (because they 

lie outside of the image plane). Analysis of the WRF model at each 15-minute interval in the set 

of five received reveals little difference in most atmospheric parameters over this 75-minute 

period, indicating the usefulness of this model for all data collected on the 17th. 

The modeled data shown above and the other datasets produced by STAR apply to specific 

latitude or longitude bands, as indicated in Figure 285 and Figure 293. In addition to these charts 

GTRI has received grid-point data permitting the creating of similar models along any line-ofsight across Colorado’s geography. These grid-points allowed GTRI to create a map of weather 

conditions for azimuth lines bisecting the Hyper-Cam FOV during the three runs depicted in 

Figure 283. These modeled data, after identification and summation, help indicate the expected 

spatial radiance patterns in our collected dataset. 

Figure 294 (temperature and wind vectors), Figure 296 (relative humidity), and Figure 298 

(graphical turbulence) from the north-south line-of-sight set are most useful for informing 

expectations as we continued to process the data collected that day. High winds and temperature 

variations created a dynamic, highly varying sky background. Relative humidity variations are 

traceable using H2O vapor detection algorithms, and the predicted turbulence of Figure 298 helps 

us understand when the varying atmospheric conditions plotted conspire to create the desired 

effect. 
222 

Figure 284. In this map of turbulence at 4.1km elevation, the blue cross marks the MRS test location.
223 

Figure 285. The red East-West line indicates the line along which are modeled the atmospheric 

conditions in the following figures. 
224 

Figure 286. In this figure the blue cross marks the MRS test location and the elevation lines indicate 

the upper and lower elevation angles in the Hyper-Cam FOV of Run 5 collected on November 17th. 
225 

Figure 287. Potential temperature for the same East-West viewing line. 
226 

Figure 288. Relative humidity for the same East-West viewing line.
227 

Figure 289. Vertical wind velocity for the same East-West viewing line.
228 

Figure 290. Predicted turbulence for same East-West viewing line.
229 

Figure 291. Predicted Turbulence Indicator for the same East-West viewing line.
230 

Figure 292. Logarithmic plot of Cn

2

 structure constant.
231 

Figure 293. Down-looking view of North-South line intersecting the MRS site. The following charts 

display predicted data along this longitude line.
232 

Figure 294. Modeled air temperature and wind vectors. The blue lines again indicate the elevation 

angles bounding the Hyper-Cam field-of-view, this time for Run 8 looking SSE.
233 

Figure 295. Potential temperature for the same North-South viewing line.
234 

Figure 296. Relative humidity along the same North-South viewing line.
235 

Figure 297. Vertical wind velocity along the same North-South viewing line.
236 

Figure 298. Predicted turbulence along same North-South line through the test site.
237 

Figure 299. Predicted turbulence indicator for the same North-South view line.
238 

Figure 300. Predicted atmospheric structure constant Cn

2

. 
239 

Figure 301. Dew Point Indicator for test location and time. 
240 

5.4.3.2 Observer Geometry and Hyper-Cam FOV in WRF Data Cubes 

The WRF data cube had 198 x 198 x 64 voxels in longitude-latitude-altitude dimensions which 

correspond to the boundaries given in Table 13. The altitude levels started at 1km, increased to 6 

km in 100 meter increments, and then continued to 21 km in 500 meter increments. 

In addition, WRF data cubes were available for five time moments from 20.00 to 21.00 UTC. The 

observation point, which corresponds to the actual test location at MRS, is given in Table 13. The 

instrument viewing geometry, which was set up to correspond to November 17, 2011, Run 5, is 

also given in Table 13. 

Table 13. Simulation set-up details 

WRF Data Cube Geographic Boundaries 

WRF Min Latitude 36.878° N 

WRF Max Latitude 43.036° N 

WRF Min Longitude 109.775° W 

WRF Min Longitude 101.423° W 

Observation Point 

Observer Altitude 2.9 km 

Observer Latitude 40.0323° N 

Observer Longitude 105.5356° W

Instrument Viewing Geometry 

Central pixel azimuth angle 164° (SSE) 

Central pixel elevation angle 13.5° 

FOV pixel size (hor x ver) 320 x 256 

FOV angular size (hor x ver) 6.4° x 5.1° 

Figure 302 and Figure 303 below show the horizontal and vertical projections of the given FOV 

in comparison with the WRF data cube boundaries. One can see that only a small part of the 

entire WRF cube gets into the instrument FOV for the given viewing geometry. For example, the 

right panel of Figure 303, which shows the vertical projection of the FOV, demonstrates that only 

the first 60/100 km of the highest/lowest rays are in the WRF data cube while the corresponding 

ray total lengths are about 300/400 km, respectively, extending from the surface to the top of the 

atmosphere (=90 km here). However, the atmosphere’s density falls off exponentially with a scale 

height of about 7 km. Therefore, only about 5% of the atmosphere is above 20 km, and the WRF 

model includes about 95% of the atmosphere. For completeness, a standard atmosphere is defined 

above this altitude and it is described in the following section. It should also be noted that the 320 

x 256 image pixel resolution for the simulations appears to be a bit excessive, especially taking 

into account that the WRF data cube vertical resolution is only 100 m, at best. 
241 

Figure 302. FOV in lat/lon coordinates (horizontal projection) for November 17, Run 5 observations. 

Figure 303. Horizontal Hyper-Cam FOV in WRF data cube boundary (left panel); vertical projection 

of Hyper-Cam FOV (right panel). Hyper-Cam FOV projections are in red; WRF data cube 

boundaries are in blue. 

5.4.3.3 The Local Standard Atmosphere 

It was necessary to define a Local Standard Atmosphere (LSA) for two main reasons: 

1. To enable radiative transfer integration up to the top of the atmosphere when the ray goes 

beyond the WRF data cube, and 

2. To estimate “the unperturbed signal” that would be observed without atmospheric 

perturbations caused by the mountain waves. 

The LSA was determined as follows: in the WRF altitude range 1-21 km the standard temperature 

and water vapor profiles were computed by averaging over all corresponding WRF altitude levels 

Longitude, W

-105.6 -105.5 -105.4 -105.3 -105.2 -105.1

39.4

39.6

39.8

40.0

Observer

Out

of

WRF

FOV
242 

(i.e., over 198 x 198 voxels for each altitude), and then the US standard atmosphere was added 

above. The agreement between these two sources is shown in Figure 304 and Figure 305. 

Figure 304. The LSA temperature profile was set to be equal to averaged WRF temperature below 21 

km (red line) and the US standard atmosphere above (black line). The green line shows WRF 

temperature variability (i.e., one standard deviation from the WRF mean). 

Figure 305. Same as Figure 304, but for water vapor. 

Figure 306 and Figure 307 show the deviation of the temperature and H2O from the LSA along 

the ray for three pixels: the bottom central pixel, the central pixel, and top central voxel. WRF 

data cube t1 = 20.00 UTC. 

Tem perature, K

200 220 240 260 280 300

0

5

10

15

20

25

30

US Std.Atm

WRF Mean Atm

W R F T-Var, K

W RF dT, K

01234

0

5

10

15

20

25

30

H 2 O , vm r

1 e -6 1 e -5 1 e -4 1 e -3 1 e -2

0

5

1 0

1 5

2 0

2 5

3 0

U S S td .A tm

W R F M e a n A tm

W RF H 2 O Var
243 

Figure 306. Temperature deviation from LSA for the three pixels.

Figure 307. H2O deviation (vmr) from the LSA for the three pixels. 

5.4.3.4 Simulation Results 

Radiances were computed with FLI-FM2 for the five time moments that were available from the 

input WRF data set: 20.00 UTC, 20.15 UTC, 20.30 UTC, 20.45 UTC, and 21.00 UTC (time 

stamps t1 to t5 respectively). Radiances were also computed for the same viewing geometry but 

for the Local Standard Atmosphere (marked t0) as a reference to an “unperturbed” atmospheric 

state. Thus, there are 6 radiance sets total, each of which contains 320 x 256 spectra (full image 

pixel resolution) degraded to 41 Hyper-Cam channels (spectral resolution ~ 16 cm-1). The 

radiance unit is equal to mW/(m2

-sr-cm-1). 

TRAY - TSTD

Distance along the ray, km

0 20 40 60 80 100

-10

-8

-6

-4

-2

0

2

4

6

8

10

Black: Bottom Cen. Pixel

Red: Cen. Pixel

Green: Top Cen. Pixel

QRAY - QSTD

Distance along the ray, km

0 20 40 60 80 100

-0.0008

-0.0006

-0.0004

-0.0002

0.0000

0.0002

Black: Bottom Cen. Pixel

Red: Cen. Pixel

Green: Top Cen. Pixel
244 

To estimate a turbulence signal, two kinds of RMS differences (obtained by summing the 

deviations squared over all 81,920 image pixels) were computed: 

1. The difference between perturbed and unperturbed radiances, and 

2. The differences between perturbed radiances at the different times. 

The latter one is most interesting in terms of comparison with what could actually be observed 

with the MRS measurements. As an example, Figure 308 presents the RMS radiance (for all 

channels) between t1 and t0 and between t4 and t0 (both are variability from the “unperturbed” 

radiances), and t4 and t1, which is the signal temporal variability. As can be seen in the figure, the 

maximum difference from the unperturbed radiances (computed for the LSA) is about 1.0 

mW/m2

-sr-cm-1, while the temporal variability is about 10 times less, i.e., 0.1 mW/m2-sr-cm-1. It 

should be noted that the FLI-FM has no ozone variability in its current version (i.e., the optical 

depth look-up-table was pre-computed for the same standard ozone profile). Thus, the variability 

in the ozone band channels (~1020-1080 cm-1) is mostly due to the temperature variations in the 

WRF part of the ray since the incoming radiance outside of the WRF cube is the same for all 

times, from t0 to t5. 

Figure 308. RMS difference for deviation from unperturbed radiances (black and red) and for 

temporal variability (green), computed for 320 x 256 image pixels. The arrows indicate the channels 

shown in the following figures at full image resolution. 

Figure 309 through Figure 313 show full-size images of the radiance difference for those channels 

where the corresponding RMS is a maximum. The images have been spatially averaged with a 

4x4 pixel window for illustrative purposes only; the numerical results have full resolution. 

wavenumber, 1/cm

800 900 1000 1100 1200 1300 1400

dR, mW / m2/ srad / cm-1

0.00

0.25

0.50

0.75

1.00

1.25

t1 vs t0

t4 vs t0

t4 vs t1
245 

Figure 309. Full-size image of R(t1) - R(t0), Hyper-Cam channel # 1. 

Figure 310. Full-size image of R(t1) - R(t0), Hyper-Cam channel # 20. 

0.5

0.6

0.7

0.8

0.9

R = R(t1) – R0, ch # 1 (c ~ 810 cm‐1)

mW /m2...

0.85

0.90

0.95

1.00

1.05

1.10

1.15

1.20

R = R(t1) – R0, ch # 20 (c ~ 1062 cm‐1)

mW /m2...
246 

Figure 311. Full-size image of R(t1) - R(t0), Hyper-Cam channel # 34. 

Figure 312. Full-size image of R(t4) - R(t1), Hyper-Cam channel # 19 (temporal variability!). 

1.10

1.15

1.20

1.25

R = R(t1) – R0, ch # 34 (c ~ 1248 cm‐1)

mW /m2...

-0.13

-0.12

-0.11

-0.10

-0.09

-0.08

-0.07

-0.06

-0.05

mW /m2...

R = R(t4) – R(t1), ch # 19 (c ~ 1049 cm‐1)
247 

Figure 313. Full-size image of R(t4) - R(t1), Hyper-Cam channel # 34 (temporal variability!). 

5.5 Why Were We Unable to Detect Turbulence? 

At 16 cm-1 resolution, the NESR is approximately 0.25 mW/m2-sr-cm-1 (see Appendix A), which 

is higher than the signal seen in the simulated time variability shown in Figure 312 and Figure 

313. Unfortunately, this indicates that the Hyper-Cam is not sensitive enough to see the signal for 

this particular case. 

Simulations run prior to the MRS field test indicated that we should have been able to see the 

turbulence with the Hyper-Cam. It has been noted that the high simulated signals could have been 

an artifact of the modeling. However, it could be possible that during the field test at MRS, we 

never collected any Hyper-Cam data of mountain wave disturbances that were high enough in 

severity to be observed. All simulations performed prior to the test were for the March 6, 2004 

severe turbulence WRF model. During the test, there were no mountain wave turbulence PIREPs 

higher than “Moderate,” and the EDRs did not show any unusual activity. Mountain waves are 

standing waves and vary only slowly with time, but temperature and water vapor concentration 

are not homogeneous at any given altitude when the wave pattern is present, and it is that 

disturbance that leads to the radiance variations across a scene as shown in previous simulations. 

On the other hand, there is always some turbulence associated with the mountain waves. 

The plan was to separate the standing wave pattern from the turbulence on the basis of the time 

scales. The D&P was going to get the fast variations caused by turbulence, and our earlier 

Boulder work led credence to that idea [34]. During the test, the D&P was tempermental and 

sometimes non-functional despite having been sent for an overhaul prior to the test, and the 

Hyper-Cam could not see evidence of the standing wave pattern. We know we had standing wave 

patterns because of the presence of lenticular clouds and the satellite image. Turbulence severity 

is undetermined, but likely not what we hoped. 

We knew that the analysis of ground-based measurements would be more challenging than flightbased measurements due to the reduced radiance signal for a slant path to space (see Section 

5.1.1, in particular, Figure 69 and Figure 72). Despite the smaller expected signal, we did not 

believe it would be so small that it would be unobservable. We believed the radiance signal 

-0.12

-0.11

-0.10

-0.09

-0.08

mW /m2...

R = R(t4) – R(t1), ch # 34 (c ~ 1248 cm‐1)
248 

would still be above the noise level of the Hyper-Cam because the FLI-FM simulations indicated 

it would. As the simulations were performed for a ground-based slant path, they inherently take 

transmission through layers of atmosphere into account. They may have over-predicted the 

signals due to the manner in which the turbulent signal was modeled. When modeling turbulence 

as a difference between a perturbed state and a standard atmosphere, a temperature offset will 

always exist, and will cause a constant bias between the two atmospheric states. For the 

simulations, the difference between perturbed radiances at different times (i.e. the standard 

atmosphere has not been subtracted) shows actual signal due to scene variability. The differences 

between perturbed and unperturbed radiances are mostly due to how the standard atmosphere was 

defined. The actual scene variability is represented by the temporal variability shown in the green 

data in Figure 308, and is unfortunately below the noise level of the Hyper-Cam. 

6 Runway Surface Conditions and Obstructions 

Emissivity images, obtained from an aircraft-mounted Forward Looking Interferometer (FLI), are 

intended to be used to alert the pilot of hazardous landing runway surface conditions and enable 

the determination of runway friction and associated stopping distance for a particular aircraft. 

This hazard detection technique is based on the fact that different surfaces have different 

emissivity spectra in the LWIR spectral region, where the FLI operates, as shown in Figure 314. 

Figure 314. Spectral emissivity of runway surface materials and water, ice, and snow. 

In a previous phase of the FLI program, the ice versus water difference was shown by their 

indices of refraction [15]. Figure 315 and Figure 316 below are reproduced from the referenced 

report. The real (reflection) and imaginary (absorption) parts of the index of refraction differ in 

the infrared. Kirchhoff’s Law states that absorption and emission terms are equivalent; thus the 

imaginary component of the index of refraction influences the emissivity of the surface. 
249 

Figure 315. Real index of refraction of water and ice in the infrared [2] [3]. 

Figure 316. Imaginary index of refraction for water and ice in the infrared [2] [3]. 

As a secondary priority, runway obstructions such as wildlife were studied briefly. Ground-based 

measurements of animals were conducted with another LWIR imager. The following sections 

outline the governing equations for determination of emissivity of surface conditions, followed by 

measurements and the development of a hazardous surface condition training set. The wildlife 

runway obscuration is discussed briefly, last. 

6.1 Governing Equations for Emissivity Determination 

The radiance as measured by the Telops Hyper-Cam can be written as: 

ܴ௦௘௡௦ሺߥሻ ൌ ܴ௦௨௥௙ሺߥሻ߬ሺߥሻ ൅ ܴ௣ሺߥሻ (6.1.1a) 

ܴ௦௘௡௦ሺߥሻ ൌ ሾܴ௘ሺߥሻ ൅ ܴ௥ሺߥሻሿ߬ሺߥሻ ൅ ܴ௣ (6.1.1b) 
250 

Where ܴ௦௘௡௦ is the at-sensor radiance, ܴ௦௨௥௙ is the at-surface radiance, ܴ௘ is the radiance emitted 

by the surface, ܴ௥ is the radiance reflected by the surface, ߬ is the atmospheric transmission, and 

ܴ௣ is the path radiance. Equation 6.1.1 has been written in two parts to explicitly show that 

ܴ௦௨௥௙ ൌ	ܴ௘ ൅ ܴ௥. 

It is assumed that the atmospheric transmittance of the short path between the Hyper-Cam and the 

surface is unity, which is particularly true for the atmospheric window region (8–12 m) 

observed with the Hyper-Cam. Therefore, with ߬~1 and ܴ௣~0 for short path lengths, then ܴ௦௘௡௦

in Equation 6.1.1 is simply the sum of the emitted and reflected radiance at the surface, i.e.: 

ܴ௦௘௡௦ሺߥሻ ൌ ܴ௘ሺߥሻ ൅ ሺߥሻ ൌ ܴ௦௨௥௙ሺߥሻ; 		߬~1, ܴ௣~0 (6.1.2) 

The emitted and reflected component of the radiance in Equations 6.1.1b and 6.1.2 can be written 

as: 

ܴ௘ሺߥሻ ൌ ߝሺߥሻܤሺߥ, ܶሻ (6.1.3a) 

ܴ௥ሺߥሻ ൌ ߩሺߥሻܴ௜ሺߥሻ ൌ ሺ1 െ ߝሺߥሻሻܴ௜ሺߥሻ (6.1.3b) 

Where ߝ is the emissivity of the surface, ܤሺܶሻ is the Planck function computed at the temperature 

of the surface, ߩ is the reflectivity of the surface, and ܴ௜ is the radiance incident on the surface. 

Note in Equation 6.1.3b that the reflectivity of the surface has been replaced by 1 – emissivity, 

following Kirchhoff’s Law. Because we are looking at rough surfaces such as concrete, the 

incident radiation comes from the entire sky dome, and ߝ is the total hemispherical emissivity. 

Equation 6.2 can now be rewritten in terms of the variables in Equations 6.1.3a and 6.1.3b: 

ܴ௦௘௡௦ሺߥሻ ൌ 	ߝሺߥሻܤሺߥ, ܶሻ ൅ ሺ1 െ ߝሺߥሻሻܴ௜ሺߥሻ (6.1.4) 

Solving for emissivity, we get: 

ߝሺߥሻ ൌ ோೞ೐೙ೞሺߥሻିோ೔ሺߥሻ

஻ሺߥ,்ሻିோ೔ሺߥሻ (6.1.5) 

The radiance observed does not represent the angularly integrated sky radiance being reflected by 

the surface. In order to account for this angular dependence, a diffuse gold plate is placed within 

the Field of Regard (FOR) of the Hyper-Cam measurements. Since gold is a nearly perfect 

reflector at infrared wavelengths, surface measurements for those pixels whose FOV were filled 

with the gold plate provided measurements of the diffuse sky radiance needed for the 

computation of emissivity from the radiance for pixels viewing the surrounding surfaces. In order 

to estimate the diffuse sky radiance for those pixels not viewing the gold plate, a spectral 

diffusivity factor to be multiplied by the observed sky radiance was defined from those pixels 

viewing the gold plate. The diffuse sky radiance, RDSKY, for each wavelength was computed from 

the observed gold plate radiance, RG, using Equation 6.1.4. 
251 

(6.1.6)

where εG and TG are the emissivity and temperature of the gold plate, respectively. Note that the 

emissivity of the gold plate is not dependent on wavenumber; because it is very low (~0.03) and 

constant over the wavelength range we use the value calibrated by Labsphere. 

The surface skin temperature is defined as the value that minimizes the local spectral variance in 

the emissivity calculated using Equation 6.1.5. The emissivity calculated according to 

Equation 6.1.5 exhibits excessive local spectral variance for an incorrect surface skin temperature 

since atmospheric emission line features observed in the sky radiance propagate into the 

calculated surface emissivity spectrum [35]. Since the local spectral variance of the true surface 

emissivity is small compared to that produced by atmospheric emission line features, the correct 

surface skin temperature must be that which produces minimal local spectral variance in the 

derived surface emissivity spectrum. The local variance of the surface emissivity spectrum is 

calculated as the standard deviation of the spectral derivatives of emissivity around every spectral 

point. The correct surface skin temperature, and associated surface emissivity spectrum, is 

assumed to be that which has the minimum local spectral variance of the calculated surface 

emissivity. Kanani et al [36] represent the smoothness of the emissivity, Sm(T) as a minimization 

of the following function with respect to temperature: 

 (6.1.7)

6.1.1 Constraints on Emissivity Determination 

The MLEV (Minimization of Local Emissivity Variance) method has been successfully used on 

datasets collected under the FLI program which will be described in the next section. This method 

allows us to determine the surface temperature and emissivity if we know both the at-sensor 

radiance and the incident radiance. However, all analysis which produced good results thus far 

has been constrained by the following: 

1. There must be a Lambertian reflector (we use a roughened gold plate) in the scene in 

order to measure the reflected radiance component, integrated over the sky dome. 

2. The path length between sensor and scene must be relatively short so that path radiance 

and atmospheric transmission can be neglected. 

In all datasets where we were able to obtain good results with the MLEV method, the incident 

radiance was obtained from the gold plate measurement. We believe we have been unable to get 

emissivity results without the gold plate measurement for the following reasons: 

1. As described above, one of the reasons why the MLEV method works so well is that it 

minimizes the high spectral variance in the incident radiance spectrum. If the 

measurement of the gold standard is flawed slightly (or, if the estimation of the incident 

radiance is inaccurate), it can have significant effects on the resulting emissivity 

calculation, because the assumptions under which the algorithm was created are now 

violated. This is probably one of the reasons why our attempts at estimating the reflected 

component were unsuccessful. This effect is shown quantitatively in Figure	317: when 

the radiance of the gold standard was changed by 1%, and the resulting emissivity of the 
252 

surface changed by up to 400%. This large error is due to the fact that the MLEV 

algorithm is no longer converging; because it does not converge, the 400% change is not 

really an accurate representation of the error. There is actually no error that can be 

measured, because there is no solution. The corresponding is shown in Figure 317. 

2. As shown in Equation 6.1.5, the surface emissivity is not proportional to the incident 

radiance. Therefore, one cannot expect to obtain the apparent surface emissivity (i.e., the 

magnitude is incorrect but the general shape of the emissivity curve is maintained) by 

neglecting the ܮ ௜term when classifying various materials. This also explains why 

attempts to constrain the emissivity (e.g. don’t allow values over one, or set the 

maximum value at some predetermined wavenumber) were unsuccessful. 

3. Using a measurement of sky radiance recorded with the Hyper-Cam as incident radiance 

is not accurate because the measurement is too directional. The benefit of the gold plate is 

that it is diffuse and therefore provides an accurate representation of background radiance 

from the entire dome above the horizon. 

Figure 317. Percent change in material emissivity due to 1% change in gold standard signature. Note 

the average value of each material given as part of the figure legend. 

The ultimate goal is to find the emissivity of the surface, but as shown in Equation 6.1.5, to 

compute it accurately we need the at sensor radiance, the radiance incident on the surface, and the 

surface temperature for computation of the Planck function. Of these three variables, the only 

thing we will have in a practical FLI application is the at sensor radiance, because it is what a 

radiometric sensor measures. The incident radiance must be measured by the gold plate or 

another diffuse target of very low emissivity, and the surface temperature must be measured by 

contact measurement (for an accurate measurement with an IR radiometer one would need to 

input the target emissivity, which is unknown). 

6.1.2 In-Scene Atmospheric Compensation 
253 

The ISAC (In-Scene Atmospheric Compensation) method [37] was identified because for a 

practical FLI application (in addition to not having a gold plate in the scene), the path lengths 

might be long enough such that the assumption of ߬~1 and ܴ௣~0 might be invalid. The ISAC 

methodology uses the statistics of the data in the scene itself to estimate the transmission and path 

radiance. 

A blackbody has an emissivity of 1 and it reflects nothing; therefore, for blackbody-like surfaces, 

ܴ௘ ൌ ܤሺܶሻ and ܴ௥ ൌ 0. We can then rewrite the radiative transfer equation for these surfaces 

(Equation 6.1.1) as: 

ܴ௦௘௡௦ሺߥሻ ൌ ܤሺߥ, ܶሻ߬ሺߥሻ ൅ ܴ௣ሺߥሻ (6.1.8) 

Assuming that most pixels in the scene are blackbody-like (i.e. ߝ~	1.0 (is valid for the satellite 

observations for which the algorithm was developed. In our case, the concrete runways are not 

blackbody-like, but given enough other background pixels, such as grass, we believe the 

algorithm is worth investigation. Equation 6.1.8 is the equation of the best-fit line with slope 

equal to atmospheric transmission and the intercept equal to path radiance, if one plots measured 

radiance, ܴ௦௘௡௦, vs. the theoretical Planck, B, for every pixel. The theoretical Planck function is 

computed for the temperature at the wavenumber index where the brightness temperature of the 

entire datacube is found to have a maximum value. Because each Telops datacube can have up to 

320 x 256 (81,920) pixels, depending on the scene, there should be enough blackbody-like pixels 

to fit a line to a scatterplot. The authors of the ISAC method recommend fitting the line to the top 

of the plot, since the blackbody-like pixels will lie there. This analysis is done for every 

wavenumber in the cube; a unique scatterplot is created at each wavenumber, thus producing ߬

and ܴ௉ as a function of wavenumber. 

Once ߬ and ܴ௣ are determined from the line parameters, the surface radiance can be found by 

solving Equation 6.1.1a: 

ܴ௦௨௥௙ሺߥሻ ൌ ோೞ೐೙ೞሺߥሻିோ೛ሺߥሻ

ఛሺߥሻ (6.1.9) 

The ISAC algorithm thus provides a method to determine the at-surface radiance from the data 

itself; however, how does one determine the emissivity? The at-surface radiance still depends on 

both the emitted and reflected components of radiance, i.e.: 

ܴ௦௨௥௙ሺߥሻ ൌ 	ߝሺߥሻܤሺߥ, ܶሻ ൅ ሺ1 െ ߝሺߥሻሻܴ௜ሺߥሻ (6.1.10) 

Setting Equation 6.1.9 equal to Equation 6.1.10 and solving for emissivity does not produce as 

clean a result as shown in Equation 6.1.5; instead, when ߬ and ܴ௉ must be considered, the 

emissivity becomes: 

ߝሺߥሻ ൌ ቀ

௅ೞ೐೙ೞሺఔሻି௅೛ሺఔሻ

ఛሺఔሻ െ ܮ௜ሺߥሻቁ ቀ ଵ

஻ሺఔ,்ሻି௅೔ሺఔሻቁ (6.11) 

Note that, for ߬~1 and ܴ௣~0, Equation 6.1.11 reduces to Equation 6.1.5, as it should, but we are 

still left with the same problem. The incident radiance and the at-surface temperature are still 
254 

unknown. We could again try to minimize the variance in emissivity spectra, but we still need a 

gold plate measurement in order to provide an accurate estimate of ܴ௜. We have been unable to 

identify a replacement for the gold plate. The results of several measurements that were 

successful under the constraints described above are shown in the following section. 

6.2 Surface Emissivity Measurements 

Surface emissivity and skin temperature measurements were conducted with the Hyper-Cam 

spectrometer for multiple scenes representing hazardous runway conditions. Ground-based 

measurements taken under this program included ice; snow; wet/dry asphalt and concrete; and 

runway surroundings/hazards such as terrain, wildlife, and vehicles. 

6.2.1 Staged Ice and Water: September 2010 

An experiment was conducted under clear sky conditions in September 2010 to simulate the 

hyperspectral emissivity imaging of runways under different surface conditions. A parking deck 

located next to Baker Building on the Georgia Tech campus provided an ideal location; the top 

level was high enough to allow for a view of the sky almost to the horizon. Additionally, the 

concrete surface on one half of the deck was treated with a non-skid coating, allowing two 

surfaces to be imaged. The experimental setup is shown in Figure 318. 

Figure 318. Parking deck set-up on Georgia Tech campus (a); Elevation view of test set-up (b) 

The Labsphere InfraGold plate [shown in Figure 318 (a)] has an emissivity of approximately 0.03 

in the LWIR. The plate enables a measurement of the diffuse reflection of the downwelling 

radiance from the sky in the same scene as the surface targets. The Hyper-Cam was initially 

pointed down at the scene at a 9 declination angle. After each downward-looking scene image, 

the Hyper-Cam was then pointed up 9 at the sky to measure the specular sky radiance 

component. The elevation view is shown in Figure 318 (b). A diffusivity factor determined from 

the ratio of the gold target radiance to the sky radiance was applied to enable estimation of the 

surface emissivity and skin temperature for every pixel within the imaging array of the 

Hyper-Cam instrument. The FOR of the Hyper-Cam results from a 320 x 256 detector pixel 

array, with each pixel having a Field of View (FOV) of 0.35 mrad. The unapodized spectral 

resolution is 0.5 cm-1. The radiance spectra were calibrated using two blackbody references, with 
255 

temperatures of 1.0 C and 55.0 C respectively. The sample integration time was 200 s. 

Because the scene to be imaged was static, an average of ten interferograms per blackbody 

measurement and ten interferograms per scene measurement was made to reduce the effect of 

detector noise. The instrument settings are given in Table 14. 

Table 14. Hyper-Cam instrument settings for parking deck test. 

Interferograms Averaged per Blackbody 10 

Interferograms Averaged per Scene 10 

FOV 320 x 256 

Resolution 1 cm-1

Integration Time 200 μs 

Cold Blackbody 1.0°C 

Warm Blackbody 55.0°C 

The surface scene consisted of wet, dry, and ice-covered concrete and wet, dry, and ice-covered 

non-skid surfaces (note that the concrete temperature was above freezing). The different surface 

conditions imaged are given in Table 15, where "bare concrete" refers to the concrete that has not 

been treated with the non-skid coating; "wet" or "dry" indicates whether water has been poured 

on the surface. 

Table 15. Surface conditions measured during parking deck test. 

Time Scene Bare Concrete 

Temp (°C) 

Non-skid Concrete 

Temp (°C) 

Gold Plate 

Temp (°C) 

7:22AM Dry surface conditions, 

before sunrise Dry – 23.4, 22.3 Dry – 23.5, 23.5 24.2, 24.1 

7:23AM Sky scene n/a n/a n/a 

8:07AM Dry surface conditions, sun 

is low in sky Dry – 25.3, 25.5 Dry – 25.2, 25.3 25.8, 25.9 

 Sky scene n/a n/a n/a 

8:52AM Dry & Wet concrete surface 

scene 

Wet - 25.2, 25.2 

Dry – 26.7, 26.0, 26.2 

Wet only – 24.9, 

24.9 27.0 

9:06AM Sky scene n/a n/a n/a 

11:03AM Dry concrete surface 

conditions Dry – 35.3, 35.7 Dry – 36.3, 35.8 36.3 

11:15AM Sky scene n/a n/a n/a 

~11:45AM Water and ice in scene (Ice 

cube 0.1C, water 6.5C) 

Dry – 39.6, 

Wet – 33.6 

Dry – 35.8, 

Wet – 18.1 38.0 

12:14PM Sky scene n/a n/a n/a 

The experiment was conducted to acquire Hyper-Cam data for a variety of possible runway 

surface conditions from which the surface emissivity, and in principle surface friction and aircraft 

stopping distance, can be determined. The wet concrete and non-skid concrete (referred to below 

as “asphalt”) conditions were observed for different surface temperature and water temperature 

contrast conditions (i.e. warm water over cold concrete and asphalt and cold water over warm 

concrete and asphalt, as provided by the melting of the ice). 

6.2.1.1 Hyper-Cam Radiance Filtering 

The first step in computing surface emissivity from the Hyper-Cam data was to filter detector 

noise. This filtering was performed by first identifying the bad (i.e. high noise) pixels in the array 

and then replacing them with an average of the surrounding good pixel values. Here, the bad 
256 

pixels were identified using the sky view data, which have a near-constant scene radiance. The 

algorithm consisted of considering an adjacent 3 x 3 array of data and identifying any outliers 

from all the differences between each pixel and all of its neighbors. Any pixel was identified as a 

“bad pixel” if its difference with all neighboring pixel values exceeded one standard deviation of 

the differences for all pixels within the 3 x 3 array. After the bad pixels were identified, the 

original sky-view and surface-view radiance values for the bad pixels were replaced with an 

average of all radiance values for good pixels surrounding the bad pixel.

6.2.1.2 Diffuse Sky Radiance Estimation 

An average of this diffusivity factor for all pixels viewing the gold plate was used to estimate the 

diffuse sky radiance from the sky radiance observed for each pixel in the Hyper-Cam array. 

Figure 319 shows the average gold plate pixel spectral radiance, along with the sky radiance 

observed with the same pixels and the spectral diffusivity factor defined as the ratio of the gold 

plate and sky view radiance values. 

Figure 319. Average surface radiance spectra for pixels viewing the gold plate and average sky 

radiance for the same pixels viewing that viewed the gold plate. 

6.2.1.3 Surface Temperature and Emissivity Determination 

The surface temperature and associated spectral emissivity were derived by considering all 

temperatures within the range of 260 K to 360 K using a five-step iterative process. In the first 

step, an increment of 10 K was used to define the temperature that minimized the standard 

deviation of the local spectral derivative of the calculated surface spectral emissivity. Once this 

temperature was defined, then a 2 increment was used within a 20 interval centered on the firstdefined surface temperature value. The iterative process was repeated a third, fourth, and final 

time, reducing the increment by a factor of 10 after each iteration, to provide a final surface 

temperature resolution of mK. 

Figure 320 shows the spectral emissivities along with the derived surface skin temperatures. 

Significant differences are evident in the emissivities, depending on the surface condition, thereby 

providing a significant measure of the surface condition. 
257 

Figure 320. Spectral emissivity of different surface types (i.e. dry concrete, wet concrete, wet nonskid pavement, and dry non-skid pavement) retrieved from radiance spectra observed with the 

Hyper-Cam instrument 

As can be seen, ice and water have very little spectral variation, whereas the dry concrete and 

non-skid surfaces provide large variations in spectral emissivity, dependent on their coverage 

with ice and/or water. Most interesting is the apparent accuracy of the surface skin temperature 

retrieved as that value which minimizes the local surface spectral emissivity variation. The in-situ 

measurements indicated the following surface temperatures: the dry concrete = 40 oC (313 K); 

wet concrete = 34 oC (307 K); dry non-skid pavement = 36 oC (309 K); wet non-skid 

pavement = 18 oC (291 K); ice = 0 oC (273 K); and the ice water = 7 oC (280 K). The retrieved 

surface temperatures shown in Figure 321 for these different surface conditions are in excellent 

agreement with these in-situ surface temperature measurements. It is clearly seen that the major 

spectral emissivity signal of different surface conditions is within the 1000–1250 cm-1 (8–10 m) 

spectral region. It is this spectral region that is most useful for estimating runway surface 

condition and potentially aircraft stopping distance from FLI measurements. 
258 

Figure 321. Derived surface emissivity for three wavelengths and the derived surface skin 

temperature (lower right hand panel). The dark spots are missing data due to the fact that a local 

emissivity variance minimum was not found in the retrieval process. 

6.2.2 Naturally Occurring Ice: January 2011 

Atlanta was hit with a rare snowstorm on the night of January 9, 2011; subsequent freezing 

temperatures all week led to ice on the roads for days. The parking lot south of GTRI’s Baker 

Building provided an excellent location for a Hyper-Cam data collection. There were several 

surfaces which simulated different runway conditions: bare asphalt; wet asphalt; ice; and snow. 

The Hyper-Cam data were collected on January 13, 2011, four days after the snowfall, so it 

should be noted that the snow had developed a thick, icy crust. A photograph of the scene is 

shown in Figure 322. 
259 

Figure 322. Parking lot south of the Baker Building, GTRI, on January 13, 2011. 

The ice in the foreground of Figure 322 was beginning to turn slushy at the edges. However, 

beyond the bare asphalt the sheet of ice was still very solid. That area was selected for subsequent 

analysis. The asphalt in the middle of Figure 322 was wet and dry in patches; it was difficult to 

see the difference in either visible or IR images and so several sections of pixels were chosen for 

analysis. The data were collected at 4 cm-1 resolution, and the Labsphere Infragold plate was 

placed in the scene and the temperature recorded. 

As described previously, when the temperature that minimizes the local variance in emissivity is 

found, it is recorded as the apparent temperature and it used to recalculate the emissivity. The 

temperatures and emissivities for the ice and snow data were obtained in this manner. The results 

of the radiance measurements and emissivity derivations are shown in the figures below. 

Figure 323. Radiances as measured by the Telops Hyper-Cam of surfaces on the Baker parking lot 

on January 13, 2011. 
260 

The three gold sample spectra in Figure 323 represent the diffusely reflected downwelling 

radiance. The radiances of all other materials in this figure (asphalt, ice, and snow) contain some 

of that reflected component, which had not yet been subtracted out. After the reflected component 

is subtracted, the emissivities for each material were calculated by the minimizing the variance in 

emissivity. The retrieved temperatures are given in Table 16. 

Table 16. Derived apparent sample temperatures. 

Sample Apparent Temperature (°C) 

Asphalt 1 5.08 

Asphalt 2 1.59 

Asphalt 3 6.39 

Ice 1 0.55 

Ice 2 -0.53 

Snow 1 -0.73 

Snow 2 1.33 

Figure 324. The emissivities of the surfaces on the Baker parking lot. 

Figure 324 shows the emissivities calculated from the apparent temperatures in Table 16. The 

results, while somewhat noisy, clearly show a difference in the shape of the emissivity of asphalt 

versus snow or ice. Figure 325 shows this more clearly, where average spectra are shown for each 

material. 
261 

Figure 325. Average material emissivities. 

The difference between the ice and snow emissivity spectra and the asphalt emissivity spectrum is 

clearly evident in Figure 325. As can be seen in Figure 323, none of the radiance spectra has a 

unique spectral feature that stands out from the rest. The feature in Figure 323 between 1000 and 

1100 cm-1 that appears in the asphalt, ice, and snow radiance spectra is most likely due to the 

reflected sky radiance. These results are consistent with those from the September 2010 field 

measurements. 

While the differences have been noted in Figure 325, it is also noted that the ice and snow spectra 

are very similar. As noted earlier, the snow was four days old and had developed a crust; this 

could account for the similarities. The difference in magnitude could be attributed to surface 

roughness and/or differences in viewing angle, because the snow was on a hill on the opposite 

side of the parking lot. 

6.2.3 MRS Emissivity Measurements: November 2011 

On November 19, 2011, there was an opportunity to measure the emissivity of natural snow and 

concrete in the MRS parking lot. An example of the results for one run is shown here. The 

technique for determining emissivity from the November 19 Telops Hyper-Cam data consisted of 

the following steps: 

1. Ingest 1 cm-1 spectral resolution calibrated radiances and replacing bad pixel data with 

neighboring values; 

2. Perform a 10 x 10 x 10 3-D boxcar smoothing of the data to minimize random noise; 

3. Define the sky radiance from the radiance observed for the gold plate within the field of 

regard assuming that: 

 (6.2.1)
262 

Where RM(ν) is the measured radiance, RG is the reflectance of gold, assumed to be 0.99, 

and TG is the temperature of the gold plate which was measured to be 274.6 K. B(ν,TG) is 

the Planck radiance corresponding to the spectral frequency ν and the temperature TG; 

4. Create a cube of spectral mean brightness temperature for spectral frequencies 

800–1200 cm-1; 

5. Compute the cube average spectral mean brightness temperature; 

6. Define snow and concrete fields of view assuming that any spectral mean brightness less 

than the cube average was snow and any spectral mean brightness temperature greater 

than or equal to the cube average was concrete; and 

7. Define the spectral emissivity, ε(ν), of the surface using the relation in Equation 6.1.5, 

where the surface temperature was measured to be 273.7 K for the snow and 275 K for 

the concrete within the Hyper-Cam’s field of regard. 

Figure 326 shows the results of a single run of Hyper-Cam measurements. Shown are the spectral 

mean brightness temperature and the emissivity for the 850, 1000, and 1150 cm-1 spectral 

channels, respectively. One can see that the mean brightness temperature is less than the physical 

temperature of the surface, dependent upon the surface emissivity. Although the surface 

temperature was assumed to be equal to the physical measurements taken in-situ for this case, in 

practice the physical temperature must also be retrieved simultaneously with the spectral 

emissivity for the runway friction determination application. 

Figure 327 shows the cube average spectrum of emissivity derived for the snow and ice 

conditions. Surprisingly the snow spectral emissivity is considerably smaller than that for 

concrete, a result that requires further validation. The relatively low snow surface emissivity may 

be due to: (1) the off-axis angle of the measurements; (2) an unrepresentative temperature 

measured in-situ for the snow; and/or (3) an incorrect estimate of the gold plate reflectivity used 

to compute the sky radiance from the gold plate measurements. 
263 

Figure 326. Hyper-Cam measurements for run “snow_scene_0000022941” taken on November 19, 

2011 at the Mountain Research Station in Colorado. The upper left panel shows the spectral mean 

brightness temperature whereas the remaining three panels show the emissivity for the 850, 1000, 

and 1150 cm-1 channels, respectively. 

Figure 327. Cube average emissivity spectrum for snow and concrete. 

6.2.4 Staged Ice and Water: Winter 2012 
264 

Hyper-Cam measurements of a second test on the parking deck on the Georgia Tech campus were 

made on November 20, 2012. The measurements were made to see if sky could be used as a 

substitute for the reflected component in the calculation of emissivity. 

The field test was set up to look at a scene similar to the parking deck test from September 2010 

(Section 6.2.1), where a scene was staged to look at dry and wet concrete and ice. In those 

measurements, the gold standard was placed in the scene because it is a diffuse and nearly perfect 

reflector of the background in the infrared. As previously described, the gold plate is necessary 

for retrieval of true emissivity because the radiance measured at the sensor is made up of both 

emitted and reflected radiance from the target. The development of a methodology for estimation 

of apparent emissivity when the reflected component is unknown is desirable. A potential method 

used sky spectra as an estimate of what would be reflected by the gold plate. However, after 

looking through all previously collected datasets, a datacube with enough foreground and sky 

could not be found. Therefore, in the November 20, 2012 test, the scene was staged to include the 

dry and wet concrete and ice, as well as enough sky for analysis. 

A Google map view of the test site and set up is shown in Figure 328. During the test, the wide 

field of view optic (25.6° x 20.4°) was used and the Hyper-Cam was viewing west. There were no 

buildings in the field of view, only the parking deck scene and the sky. 

Figure 328. Emissivity test plan set-up. 

A radiance image of one of the datacubes is shown below in Figure 329. This image is from a 

datacube recorded at 6:40 AM (EST), right before sunrise. All datacubes were collected at 1 cm-1

resolution because the Minimum Local Emissivity Variance (MLEV) method depends upon the 

narrow spectral lines that appear due to water and other molecules in the atmosphere. Note that 

the gold standard is still in the scene (in red box) for comparison of the apparent emissivity 

algorithms with the true calculation. 
265 

Figure 329. Radiance image of parking deck and sky at 6:40 AM EST. The radiance image is an 

average over all bands (excepting the first and last 50), Radiance units W/cm-1m2

sr.

A second set of Hyper-Cam measurements on the parking deck on the Georgia Tech campus were 

made on December 19, 2012. These measurements were conducted in the exact same manner as 

the measurements made in November 2012 so are not described again. The only difference was 

that the December test was planned for a clear day (in November it was cloudy), and the narrow 

FOV optic was used to increase the spatial resolution. The set-up is shown below in Figure 330. 

The FOV from the November test and the FOV from the December test are shown in this figure 

and are represented by solid and dashed red lines, respectively. In November, the FOV was 

25.6° x 20.4°, and in December the FOV was 6.4° x 5.1° (horizontal x vertical). 

Figure 330. Emissivity test plan set-up. The solid lines represent the FOV used in the November test; 

the dashed lines represent the FOV used in the December test.

An averaged radiance image from the parking deck test conducted on December 19 is shown in 

Figure 331. In this scene, the Hyper-Cam is positioned on the parking deck looking sideways. 

Therefore, the scene is looking along the parking deck with a building behind it and the sky 

above. In this image, the ice and water are circled in yellow and red, respectively. The gold 
266 

reflectance standard was also in the scene; however, due to the narrow field of view there are not 

enough pixels on target to distinguish it in the image. The gold reflectance standard radiance is 

not crucial to the following analysis; the standard was placed in the scene only in case we wished 

to analyze the data as we have done previously. Therefore not having enough pixels on that target 

is not detrimental to the analysis. 

Figure 331. The averaged radiance datacube from the Dec 19 test. The ice is circled in yellow, and the 

water is circled in red. Radiance units W/m2

cm-1sr.

As described, the purpose of this test was to see if the radiance of any sky pixels could be used as 

a substitute for the radiance of the gold plate pixels. This technique did not work, as we described 

in Section 6.1.1. Therefore, because this dataset had longer path lengths then all previous sets, we 

decided to test the ISAC method that was described in Section 6.1.2. 

The first step of this method was to calculate the brightness temperatures for each datacube. This 

was done for one datacube from November 20 and one datacube from December 19. Note the 

sizes of both images were cropped in all three dimensions to save processing time. Then, the 

wavenumber index where the brightness temperature is a maximum value was found for both 

images. These maxima occurred at 1259.9 and 1257.4 cm-1 for the November 20 and December 

19 images, respectively. The brightness temperature images are shown below in Figure 332. 
267 

Figure 332. Maximum brightness temperature images for November 20 (left) and December 19 

(right) datasets, brightness temperature in Kelvin. 

After the maximum wavenumber index was found, the Planck function was calculated for all 

pixels for the brightness temperature at this index. This calculation was performed for the two 

images shown in Figure 332. Then, a scatterplot was created of the measured radiance vs. the 

calculated Planck function for all pixels in each image. These plots are shown below in Figure 

333. 

Figure 333. Scatterplot of measured radiance vs. Planck calculated radiance for November 20 (left) 

and December 19 (right) datasets. Data shown at 1259.9 and 1257.4 cm-1, respectively. Radiance units 

W/m2

cm-1sr. 

The plots shown above indicate that the measured radiance is nearly equal to the Planck 

computed radiance for both November and December datasets. They are very close due to the 

manner in which the Planck radiance was computed; these plots show the measured vs. Planck 

radiance at the wavenumber index where the brightness temperature was found to be maximum. 

These scatterplots were then created for every wavenumber index in the datacube for the 
268 

brightness temperature at the maximum. Scatterplots at 965.7 cm-1 are shown below in Figure 

334 for the two datasets. 

Figure 334. Scatterplot of measured radiance vs. Planck calculated radiance for November 20 (left) 

and December 19 (right) datasets. Data shown at 965.7 cm-1. Radiance in W/m2

cm-1sr. 

Lines were fit to the top-most pixels in the scatterplots to find the atmospheric transmission and 

path radiance (see Equation 6.1.8). However, we were unable generate reasonable results. We 

believe that there were not enough blackbody-like pixels in this scene (mostly concrete, which 

has Reststrahlen feature), which we knew prior to the analysis but decided to try anyway. Another 

data collection was planned to collect more data to test the ISAC method; however, given that we 

could not find a way to eliminate the gold plate measurement, the data collection was cancelled. 

6.3 Surface Emissivity Retrieval Algorithm 

The radiative transfer equation governing the radiance spectrum observed with a FLI on an 

aircraft approaching an airport runway is 

(6.3.1)

Where the first term is the surface radiance reaching the FLI, the second term is the surface 

reflected downwelling atmospheric radiance reaching the FLI, and the last term is the 

atmospheric emission between the aircraft level and the surface received by the FLI. 

The retrieval model used is a radiance eigenvector (EOF) regression model where the runway 

surface emissivity spectrum ε(ν,θ) and skin temperature TS are predicted from a linear 

combination of radiance eigenvector coefficients. The matrix of radiance eigenvectors was 

computed from a training set of simulated radiances. 

LBLRTM was used to produce a statistical training set of spectra based upon the September 2010 

surface emissivity measurements described in Section 6.2.1. Accurate line-by-line calculations 

provide us with more line signatures in the output spectrum due to the reflected term in 
269 

Equation 6.3.1, which in turn leads to more information content in the measurements, because it 

allows us to unlink the emissivity and skin temperature signals in the measured spectrum. 

The surface emissivity spectrum for the training set of radiances was represented by a linear 

model as 

(6.3.2)

Where wi are weights for the combination of five possible runway surface emissivity types, ε

*

i.k

(i.e. asphalt, concrete, water, ice, and snow) of Equation 6.3.2 and shown in Figure 335. 

Figure 335. Spectral emissivities of different surface types assumed for development of the training 

set. 

The radiance/emissivity training data set was generated using LBLRTM for the conditions where 

aircraft altitudes were 100 m, 300 m, and 500 m. The viewing angle was fixed and equal to 5.7 

degrees below the horizon, which corresponds to 1 km, 3 km, and 5 km distances to the runway, 

respectively. For each altitude, the ground air temperature Tair was uniformly distributed within a 

260 K to 320 K range, in 5 K increments. The surface temperature was randomly distributed 

around the ground air temperature by adding Gaussian noise with bias 0 K and sigma 3 K. The 

spectral range was 850–1200 cm-1 and the spectral resolution was 1 cm-1. Different runway 

surface emissivity spectra were generated by selecting the wi’s from a uniform distribution of 

random numbers ranging between 0 and 1, with the results scaled so that the sum of the wi’s 

equaled unity. The emissivity weights were additionally constrained so that w(water) = 0 when 

the surface temperature was below 270 K, and the weights for ice and snow were set equal to zero 

when the surface temperature exceeded 275 K. A total of 500 randomly generated surface 

emissivity spectra and skin temperatures were used to produce FLI radiance spectra for each of 

the 13 ground air temperature conditions, yielding 6,500 different runway surface conditions for 

given aircraft altitude. Clear skies were assumed for all of this analysis. Empirical orthogonal 

functions were calculated from the training set of FLI radiances for each aircraft altitude 

separately. 

W avenumber (1/cm)

800 900 1000 1100 1200

Emissivity

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1.00

Asphalt

Concrete

Water

Ice

Snow
270 

The retrieval vector includes the surface temperature and 5 weights wi for the emissivity spectrum 

of different surface types used. The same set of training radiances was used to check the accuracy 

of the retrieval, but artificial noise with NEdT = 0.1 K, 0.3 K, and 0.5 K was added to the original 

radiances for the retrieval. The accuracy of the retrieval significantly depends on the number of 

the radiance principal components (PC-scores) taken for prediction. Figure 336 shows the root 

mean square error of the retrieved surface temperature (averaged over 6500 trials for an aircraft 

altitude of 300 m) as a function of the number of PC-scores taken into account. 

Figure 336. Root Mean Square Error of the retrieved surface temperature for the 6,500 simulated 

runway surface conditions and for three different radiometric measurement noise levels; aircraft 

altitude 300 m. 

As follows from Figure 336, the optimal number of PCs ranges from 4 to 7 in terms of the best 

accuracy. The same range for the number of PCs is optimal to retrieve the weights of the 

emissivity spectra (with slight variations depending on noise level). It is worthwhile to note that 

the initial variability for the surface temperature in the set was ~18.5 K (260 – 320 K range for 

the ground air temperature Tair plus additional 3 K random variations for TS around Tair), while the 

retrieved surface temperature is within a 0.5 K statistical agreement with the “true” one. 

Figure 337 shows the root mean square error of the retrieved surface emissivity spectrum for all 

6,500 surface temperature/emissivity cases and for the three aircraft altitudes. It can be seen that 

the emissivity error significantly depends on the noise level (it almost doubles for a radiometric 

brightness temperature error of 0.5 K as opposed to 0.1 K). The figure also shows that the 

emissivity error is slightly increasing with aircraft altitude. 

# of Radiance PCs used

1 2 3 4 5 6 7 8 9 10 11 12

Tsfc RMS, K

0.0

0.5

1.0

1.5

2.0

NEdT  0.1 K

NEdT  0.3 K

NEdT  0.5 K

Alt = 300 m
271 

Figure 337. Root Mean Square Error (RMSE) of the retrieved surface emissivity spectra for the 

6,500 simulated runway surface conditions, for three different radiometric measurement noise levels, 

and for three different aircraft altitudes 

Following the development of the emissivity retrieval algorithm, it was then updated as follows: 

1. Emissivity spectra retrieved from real measurements in Atlanta, in September 2010, were 

used to generate the random set of emissivity spectra; 

2. More accurate computing of the downward radiance was accomplished; and 

3. Specular surface reflectance was replaced with diffuse (Lambertian) reflectance. 

wavenu m ber

900 10 00 1 100 1200

0 .0 0 2

0 .0 0 4

0 .0 0 6

0 .0 0 8

N E dT  0 .1 K

N E dT  0 .3 K

N E dT  0 .5 K

A lt = 1 0 0 m

w avenum ber

900 100 0 11 00 1 200

0 .0 0 2

0 .0 0 4

0 .0 0 6

0 .0 0 8

NEdT  0 .1 K

NEdT  0 .3 K

NEdT  0 .5 K

A lt = 3 0 0 m

w avenum ber

900 100 0 11 00 1 200

0 .0 0 2

0 .0 0 4

0 .0 0 6

0 .0 0 8

NEdT  0 .1 K

NEdT  0 .3 K

NEdT  0 .5 K

A lt = 5 0 0 m

w avenum ber

900 100 0 11 00 1 200

0 .0 0 2

0 .0 0 4

0 .0 0 6

0 .0 0 8

NEdT  0 .1 K

NEdT  0 .3 K

NEdT  0 .5 K

A lt = 3 0 0 m

w avenum ber

900 100 0 11 00 1 200

0 .0 0 2

0 .0 0 4

0 .0 0 6

0 .0 0 8

NEdT  0 .1 K

NEdT  0 .3 K

NEdT  0 .5 K

A lt = 5 0 0 m
272 

The emissivity spectra retrieved from real measurements made with the Hyper-Cam in Atlanta 

are presented in Figure 320. Those spectra were used as a basis to generate random emissivity 

while checking the accuracy of the regression retrieval. 

A second radiance/emissivity training data set was generated using LBLRTM. As noted above, 

the surface reflectance was changed from specular to diffuse. Figure 338 shows the difference in 

radiances due to this change. Although this difference is not large in comparison with the total 

signal, it has clear line structure, which helps to unlink surface temperature/emissivity coupling 

during retrieval. 

Figure 338. The difference in radiances for the same atmospheric state when the surface reflectance 

is specular or diffuse. 

As before, the viewing angle was fixed and equal to 5.7 degrees below horizontal, which 

corresponds to 1 km, 3 km, and 5 km distances to the runway for aircraft altitudes of 100 m, 300 

m, and 500 m, respectively. The spectral interval was the 850–1250 cm-1 range and the spectral 

resolution was 1 cm-1. Different runway surface emissivity spectra were again generated by 

selecting the wi’s from a uniform distribution of random numbers ranging between 0 and 1 with 

the results scaled so that the sum of the wi’s equaled unity. The emissivity weights were 

additionally constrained as before so that w(wet) = 0 when the surface temperature was below 

270 K, and the weight for icy concrete was set equal to zero when the surface temperature 

exceeded 275 K. A total of 500 randomly generated surface emissivity spectra and skin 

temperatures were used to produce FLI training spectra for a given aircraft altitude. 

Figure 339 shows mean radiance values and standard deviations for the training set, for an aircraft 

altitude of 300 m. The downward radiance at the surface level is shown in Figure 340. As it is 

partially reflected, it contributes to the line structure of the total observed radiance. 

R: Diffuse-Specular surface

wavenumber, cm-1

900 1000 1100 1200

W  m2 sr cm-1

0

5e-9

1e-8

2e-8

2e-8
273 

Figure 339. Mean and standard deviation for training set of radiances; aircraft altitude 300 m. 

Figure 340. Downward radiance at the surface level. 

The same set of training radiances was used to check the accuracy of the retrieval, but artificial 

noise with NEdT = 0.3 K, 0.5 K, and 1.0 K was added to the original radiances for the retrieval. 

Figure 341 below shows the root mean square error of the retrieved surface emissivity spectrum 

for an aircraft altitude of 300 m, averaged over 500 different runway conditions. Note that the 

total emissivity error over whole spectrum is about 1/100 of the added noise level, i.e. it is equal 

to ~0.003, ~0.005, and ~0.01 for NEdT = 0.3 K, 0.5 K, and 1.0 K, respectively. 

wavenumber, cm-1

900 1000 1100 1200

W  m2 sr cm-1

0.0

2.0e-6

4.0e-6

6.0e-6

8.0e-6

1.0e-5

1.2e-5

Mean

1 

Downward Radiance

wavenumber, cm-1

900 1000 1100 1200

W  m2 sr cm-1

0

1e-6

2e-6

3e-6

4e-6

5e-6

6e-6

7e-6
274 

Figure 341. Root Mean Square Error (RMSE) of the retrieved emissivity averaged over 500 

simulated runway surface conditions and for three different radiometric measurement noise levels; 

aircraft altitude 300 m. 

6.4 Surface Emissivity Conclusions 

The surface emissivity is a difficult property to calculate because it is difficult if not impossible to 

measure all variables at measurement time. It is true that for most materials the emission term 

dominates the at-surface radiance term; however, due to the spectral nature of incident radiance, it 

cannot be neglected. For emissivity calculation, we have shown that small errors in the input 

variables (of as little as 1%) lead to large errors in the output (up to 400%). The incident radiance 

is also a very difficult quantity to measure; it requires simultaneous measurement of a Lambertian 

reflector made of a low emissivity material and with known temperature. We used a roughened 

gold plate, which is standard practice, but it only exists in experimental set-ups. 

Furthermore, consider the plot shown in Figure 342. It contains emissivity of water, ice, frost, 

snow, concrete, asphalt, and asphaltic concrete. These data were compiled from JPL’s ASTER 

Spectral Library and the UCSB MODIS Emissivity Library. This figure is similar to Figure 314 

in that the same types of materials have been plotted to show the emissivity differences among 

several materials representative of runway conditions. However, note the scale of each axis on 

both figures. In Figure 314, the wavelength axis is scaled between 3 and 14 μm, and the 

emissivity axis is scaled between 0.93 and 1.0. In Figure 342 the wavenumber axis is scaled 

between 800 and 1350 cm-1, and the emissivity axis is scaled between 0.84 and 1.02. The 

wavenumber scale was chosen for this figure because it represents that which is observable with 

the Hyper-Cam; the scale corresponds to 8 to 12.5 μm. 

wavenumber

900 1000 1100 1200

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

Noise  0.3K; NeofR  8

Noise  0.5K; NeofR  7

Noise  1.0K; NeofR = 6
275 

Figure 342. Spectral emissivity of materials representative of runway surfaces [38] [39]. 

We must consider not only the emissivity differences among the surfaces, but also emissivity 

differences among the surfaces in the waveband region that is observable with the Hyper-Cam 

when making conclusions about the emissivity measurements in the previous section. In the 800 

to 1350 cm-1 region, the emissivity of ice, water, and snow are nearly identical. In Figure 314 

there is a substantial separation between the emissivity spectra of ice and water between 11 and 

14 μm, but this corresponds to ~715 to 910 cm-1, and the Hyper-Cam range overlaps from 800 to 

910 cm-1 only. If one looks at the “U” shape of the NESR of the Hyper-Cam at 1 cm-1 resolution 

(see Appendix A), it becomes apparent that most of the overlapping region is in the noise. 

Therefore, there are differences among the emissivity of the materials in the stated range of the 

Hyper-Cam, however, we must consider the differences among the emissivity of the materials in 

the effective range of the Hyper-Cam. 

Despite the limitations, we have demonstrated that we can determine material emissivity with the 

MLEV method in staged scenes. However, the overall goal is to define the requirements for a FLI 

sensor. The MLEV method requires high spectral resolution, which means that if a FLI sensor 

were to use this algorithm, it would need to be a hyperspectral sensor. The hyperspectral 

measurements were to be used as a study to determine if certain bands could be identified that 

could be used to possibly set the requirements for a broadband, duel band, or multispectral sensor 

FLI sensor. We attempted to do band comparisons in the radiance domain, but due to different 

illumination conditions among different data sets, we realized the need to work in the emissivity 

domain. We were unable to identify an appropriate temperature and emissivity separation 

algorithm that had the potential to be effectively translated from a purely research demonstration 

to a real-world, real-time FLI sensor application. 
276 

One might note in Figure 314 that there are also differences among emissivity spectra in the midwave Infrared (3 – 5 μm) region. There are sensors that operate in this waveband; but, emissivity 

measurement in the MWIR presents new challenges: in particular, there is substantial 

contribution from direct solar irradiance that may dominate the calculation, and the minimization 

technique that works in the LWIR cannot be applied to the MWIR [36]. Kanani et al [36] 

recommend minimizing the emissivity variance in the LWIR, and once the temperature is 

determined, using this temperature to compute emissivity in the MWIR. This method would again 

require a hyperspectral sensor covering the two bands. 

6.5 Runway Obscuration 

As a part of funding for another project, the GTRI research team was able to acquire LWIR 

images of animals in various conditions at Yellowstone National Park in May 2010. The 

following images were not collected with the Hyper-Cam, but rather a Fluke Ti10 Thermal 

Imager. The specifications of the thermal imager are given below in Table 17. Note that the 

spectral band of this thermal imager is the same as the Hyper-Cam. 

Table 17. Fluke Ti10 specifications. 

Field of view 23° x 17° 

Spatial resolution (IFOV) 2.5 mrad 

Minimum focus distance Thermal lens: 15 cm (6 in) 

Visible (visual) light lens: 46 cm (18 in) 

Focus Manual 

Image frequency 9 Hz refresh rate 

Detector type 160 X 120 focal plane array, uncooled microbolometer 

Infrared lens type 20 mm F = 0.8 lens 

Thermal sensitivity (NETD) ≤ 0.2 °C at 30 °C (150 mK) 

The following images were collected with the Ti10 LWIR imager. Figure 343 and Figure 344 are 

of a wolf walking on the roads at Yellowstone at night. Figure 345 is of a deer at night in the 

visible and LWIR. Figure 346 is a herd of elk at night on the grass. Figure 347 is a herd of elk in 

the daytime on the grass. These images, while not taken with the Hyper-Cam, are representative 

of images that the Hyper-Cam would take since they are both imaging in the same spectral band. 

It is evident in these images that animals are distinct from the background in both the day and the 

night. The animals stand out from the background because they are much warmer than the 

background. Also note that the pavement is differentiated from the grassy surroundings because 

of the relative temperature differences. 
277 

Figure 343. Grey Wolf at night on pavement, Yellowstone National Park: visible (left) , and LWIR 

(right). 

Figure 344: Wolf at night, on pavement. (visible images are not shown, they were black). 

Figure 345. LWIR image of deer at night at YNP in central region of composite image (visible image 

is black border). 
278 

Figure 346: Elk at night in the distance, on grass. 

Figure 347: Elk in day in the distance, on grass. 

7 Low Visibility 

Based on the sensitivity studies in the previous Phase 1 FLI project, an imaging FLI spectrometer 

is expected to provide enhanced vision in the cockpit of an aircraft in low visibility conditions. 

The image quality can be greatly enhanced by using an algorithm that retrieves the background 

scene temperature distribution from the observed radiance spectra, and the algorithm is a simple 

linear operator that could provide real-time cockpit displays of the terminal area on approach. 

The algorithm was demonstrated under a restricted set of aircraft altitude and atmospheric 

conditions. Further work was performed to demonstrate the robustness of the algorithm and to 

define the actual measurement and display characteristics of a practical system. 

7.1 Low Visibility Measurements 

Several data collection activities associated with low visibility conditions occurred. The first was 

on November 30, 2010. The Telops Hyper-Cam was set up on the fire escape of GTRI’s Baker 

Building on the Georgia Tech campus to collect data in these low visibility conditions. The 

instrument settings from the activity are given in Table 18. However, during set up and 

calibration, the fog lifted. At the time of the measurement, only the tip of the Bank of America 

Building (the tallest building in the Atlanta skyline) was still obscured. A visible image of the 

Bank of America Building is shown in Figure 348. 

Table 18. Hyper-Cam instrument settings for November 30, 2010 fog measurements. 

Cold Blackbody Temperature 10.0 °C 
279 

Warm Blackbody Temperature 35.0 °C 

Resolution 4 cm-1

FPA Size 320 x 150 

Integration Time 200 μs 

Figure 348. Nov. 30, 2010 Visible Image of Bank of America Building taken with the Hyper-Cam. 

Three additional Hyper-Cam data collections in three different visibility conditions were made 

from the fire escape of Baker Building, aimed toward the AT&T building (the building to the left 

of the Bank of America Building in Figure 348). The AT&T Building provided a background to 

see if any features could be seen in the LWIR through the fog. The data collections were made on 

January 18, 2011, at a visibility of 2 miles; on January 20, 2011, at a visibility of 6 miles; and on 

January 28, 2011, at a visibility of 10 miles. The visibility information was obtained for Atlanta, 

GA from www.wunderground.com. A Google map of Atlanta is shown in Figure 349 to show 

where the AT&T Building is relative to the GTRI measurement site. 

The view off the fire escape on the clear day, January 28, is shown in Figure 350. Of the two 

buildings in the distance that can be seen in Figure 350, the one on the left, which is the AT&T 

Building, is the target. Figure 350 is compared with Figure 351, which was taken on January 18, 

2011. Note that the two buildings in the distance are completely obscured in the reduced visibility 

condition of January 18. Also note from Figure 349 that the AT&T Building is approximately 1.4 

km (0.87 mi) from the Baker building, therefore the visibility between the measurement site and 

the target building was much less than the 2-mile visibility listed on Weather Underground the 

morning of January 18. Weather data, such as temperature and relative humidity, were collected 

on site by GTRI. These conditions are given in Table 19. 
280 

Figure 349. Google map view of Atlanta showing the AT&T Building relative to the measurement 

site. 

Table 19. Temperature and relative humidity readings for the low visibility data collection activities.

Date Temp (°C) RH (%) 

11/30/2010 23.09 48 

01/18/2011 11.90 57 

01/20/2011 13.91 49 

01/28/2011 23.97 18 

02/01/2011 10.53 69 

Figure 350. The view from the Baker building fire escape on a clear day, January 28, 2011. 
281 

Figure 351. The view from the Baker building fire escape in reduced visibility conditions, January 

18, 2011. 

Hyper-Cam datacubes were collected on January 18 and 20, 2011 at 4 cm-1 and 56 cm-1

resolution. The lower 56 cm-1 resolution datacubes were collected in addition to the 4 cm-1

resolution datacubes in the event that the spectra from the 4 cm-1 datacubes were too noisy, 

however this was not the case and the results of the 4 cm-1 data are presented here. Figure 8 

shows the IR imagery of the scene from all three days. 

Figure 352. IR image at 1076 cm-1 of the scene on January 18, 2011, visibility 2 miles (left panel). IR 

image at 1076 cm-1 of the scene on January 20, 2011, visibility 6 miles (middle panel). IR image at 

1076 cm-1 of the scene on January 28, 2011, visibility 10 miles (right panel). 

The left panel in Figure 352 is much noisier than the other two images; the sharpness increases 

with the visibility. The lack of contrast in the lower visibility images could lead to this graininess. 

The NESR’s from sky pixels all three images are compared and are shown in Figure 353. 
282 

Figure 353. NESR of selected sky pixels from January 18, 2011 (top left panel) January 20, 2011 (top 

right panel) January 28, 2011 (bottom panel), Radiance units W/cm-1m2

sr.

Two different regions in each cube were then selected for radiance comparison: a sky region and 

a building background region. The results are shown in Figure 354. 

Figure 354. Radiance comparison of building and sky at three visibilities. 

Several observations can be made from Figure 354. First, one may note that the radiance of the 

sky on January 18 and on January 20 (2 and 6 mi visibility, respectively) is nearly equivalent to 

the radiance of the AT&T Building background on January 18. In the lower visibility conditions, 

the building is completely obscured by the fog in the IR. The radiance spectra of sky itself from 

the two reduced visibility days are also relatively flat and featureless, in comparison with the 

measurement taken on January 28 under clear conditions. The sky measurement on January 28 

has spectral features that are characteristic of a sky spectrum. Note also the difference in the sky 

spectrum versus the AT&T Building spectrum on January 28 under clear conditions. This day is 

the only day of the three where the building is clearly different than the sky. On January 20 (6 

mile visibility), the radiance of the building background is only slightly higher. This data set 
283 

demonstrates that under reduced visibility conditions, the fog tends to obscure the background in 

the long-wave IR since the water is opaque in this region. 

The weather on February 2, 2011 presented another excellent opportunity to collect Hyper-Cam 

data in reduced visibility conditions. This data collection, as opposed to the fog data collected in 

January, focused on imaging concrete and asphalt through fog at as close to an aircraft descent 

angle (3 degrees) as possible. 

The Hyper-Cam was set up on the roof of the Centennial Research Building, which is next to the 

Baker Building at GTRI. Three different scenes were imaged from this roof: 1) looking east 

towards 10th Street; 2) looking south towards a parking lot on the Georgia Tech campus; and 3) 

looking southeast towards the roof of an adjacent parking deck. Figure 355 shows a Google map 

view of the test site and imaged scenes; Figure 356 through Figure 358 show the visible images 

of the imaged scenes. 

Figure 355. A Google Map view of the scenes imaged on the February 1st test. 

Figure 356. Visible image of Scene 1, looking East to 10th St.
284 

Figure 357. Visible image of Scene 2, looking South to parking lot.

Figure 358. Visible image of Scene 3, looking Southeast to parking deck. 

Neither the Hyper-Cam’s visible camera nor the Nikon could distinguish any features along 10th

Street through the fog; the visible image from the Nikon is shown in Figure 356. However, during 

the test we could visually see the traffic lights, and this visual cue was used as a guide for 

orienting the Hyper-Cam. An attempt was made to record each datacube during red lights along 

10th Street so that no cars would be moving through the scene. The data for all three scenes were 

recorded at 4 cm-1 and 56 cm-1 resolution. In addition, the parking deck of Scene 3 was recorded at 

1 cm-1 resolution (a higher resolution and longer recording time were enabled since there were no 

cars moving on the deck). Scene 1 (10th Street) and Scene 2 (parking lot) were recorded at a 

Hyper-Cam declination angle of 3 degrees to mimic the angle at which aircraft land; Scene 3 (the 

parking deck) was recorded at a 5 degree declination angle. 

After initial preview of these data sets by Hampton University, it was determined that data 

collected in thirty-minute intervals on the same day would be better suited for their analysis. The 

project team was unable to collect this sporadic weather condition during the remainder of the 

project. 
285 

8 Volcanic Ash 

Approximately 60 volcanoes worldwide are active during a typical year, and in the North Pacific 

Region of the United States alone volcanic ash is present an average of 4 days a year where most 

jet aircraft fly [40]. The Geophysical Institute at the University of Alaska Fairbanks completed an 

extensive study of volcanic activity in the North Pacific. In this study, 100 years of volcanic 

eruption data was used to run thousands of PUFF model simulations of eruptions of volcanoes in 

the area. (PUFF is a model that simulates the movement of airborne volcanic ash in 

near real-time when an eruption has occurred [41]). The result was a reliable set of average data 

describing trends in ash migration from Northern Pacific volcanoes. They found that only 35% of 

volcanic eruptions in that area over the past 100 years caused ash to reach above 8km 

or about 26,250 feet [42]. 

Volcanic Clouds contain ash and gases that are hazardous to aviation. Significant damage to 

aircraft has occurred during encounters with ash, where the ash melts and fuses to the engine 

turbine blades causing engine failure in flight. The 2010 eruption of Iceland’s Eyjafjallajokull is 

estimated to have cost the airline industry over two billion dollars. 

The Norwegian Institute for Atmospheric Research holds the patent on an airborne passive IR 

radiometer originally developed by Dr. Alfredo Jose Prata when he was employed by the 

Commonwealth Scientific and Industrial Research Organisation (CSIRO) located in Melbourne, 

Australia. The system, named the Airborne Hazards Detection System (AHDS) uses a two-band 

detection technique similar to satellite detection techniques for volcanic ash. International patent 

WO 2011/135060 A1 was issued in November 2011 to Prata and Bernado for a “System and 

method for detecting adverse atmospheric conditions ahead of an aircraft” [1]. The patented 

system has multiple infrared cameras that detect IR radiance in different bands of IR light. The 

system warns and displays the position of the adverse condition so the aircraft may be re-routed. 

Similarly, a FLI system is able to perform this capability. A good working relationship exists with 

Prata. 

9 In-Flight Icing 

As reported in the FLI Phase 1 program [16], ice and liquid water have very different optical 

properties in the infrared molecular “window” region of the spectrum starting at 11 microns. As a 

result, it should be possible to use the FLI instrument to diagnose the existence of supercooled 

liquid water in clouds (i.e., detect water droplets at sub-freezing temperatures that causes airframe 

icing) and to discriminate between ice and water on surfaces, such as runways. However, this 

hazard was not investigated because flight tests were not conducted, and the area of greatest 

differentiation between the indices of refraction for ice and water occurs outside of the effective 

spectral range of the Telops LWIR instrument. 

With the proper instruments available, the possible detection technique is described here. The 

temperature of the cloud can be observed as the brightness temperature measured with the FLI 

near 12 μm, where the cloud emissivity is a maximum, or assumed to be close to the outside air 

temperature observed independently by the aircraft measurement system. Figure 359 and Figure 

360 show that the real (reflection) and imaginary (absorption) parts of the index of refraction for 
286 

water and ice differ greatly in the range of infrared wavelengths where atmospheric molecular 

absorption is a minimum (i.e., the molecular absorption “window” between 10 and 12 μm). In 

particular, when viewing cloud ice crystals, or an icy runway surface, with an FLI, the radiance 

observed will increase rapidly between 10.5 and 12 μm, whereas when viewing water droplets, or 

a wet surface, the radiance observed will be relatively constant between 10.5 and 11 μm with the 

strong increase in radiance occurring between 11 and 12 μm. Thus, it is the small spectral shift in 

the wavelength at which the radiance emission abruptly increases across the atmospheric 

“window” region that will allow the FLI to determine whether cloud icing conditions, or a 

hazardous icy runway, may be encountered by the aircraft. There is a precedent for this type of 

discrimination; two-band IR radiometers are commercially available for detecting “black ice” on 

highways (black ice is the term for a thin layer of ice that is difficult to detect visually). 

Figure 359. Real Index of Refraction for Water and Ice in Infrared. [43][44] 
287 

Figure 360. Imaginary Index of Refraction for Water and Ice in the Infrared. [43][44] 

10 Distance to the Hazard 

In previous reports, we have discussed the ability to determine the distance to the hazard. This 

ability relied on the collection of high spectral resolution data (1 cm-1). Over the last year, we 

have shifted to lower spectral resolution data (16 cm-1) to try to detect the signal of the hazards. 

As such, we have inherently lost the ability to determine the distance to the hazard using the CO2

line structure. However, an additional dedicated detector with two narrowband filters that can 

look at the amplitudes of the CO2 lines has been proposed. Note that these lines appear in the 

mid-wave infrared (MWIR) and at the edges of the long-wave infrared (LWIR) window. 

Detection in this phase has focused on the long-wave infrared (LWIR). Therefore this dedicated 

detector would be in the MWIR, while the hazard detection detector would be in the LWIR. 

11 Defining a FLI Sensor Requirements 

Unfortunately, known imaging instruments in the wavelength ranges of interest were not found to 

meet the needs of a FLI sensor for the detection of aviation hazards. Generally, optimal 

wavelength ranges for the various signals were different, and sometimes the same hazard has a 

different wavelength range depending on flight altitude. In addition, signal strengths were below 

the noise level of the instruments used for data collection. 

11.1 Wake Vortices 

Simulations of the atmospheric temperature, moisture, and exhaust gas conditions associated with 

wake vortices from commercial jet aircraft were simulated using TASS. Wake vortex signals 

were then simulated for several different flight altitudes both LBLRTM and FLI-FM2; the results 

are shown in Section 4.1. The results of each model agree and indicate that the noise level 

necessary for wake vortex detection is on the order of milli-Kelvin. The models show that the 
288 

optimal band passes are different for a wake vortex signal simulated along a slant path from 

ground versus a horizontal path at flight altitude. However, for both elevations, there is a peak 

signal in the range of 1250 – 2000 cm-1. Therefore, if a broadband radiometer with one band from 

1250 – 2000 cm-1 could be identified, it would be optimal, as it could be used for wake vortex 

detection regardless of observer altitude. Using a broad band (~750 cm-1) imaging radiometer, 

operating in the 1250 – 2000 cm-1 band, which has a sensitivity on the order of 1 mK, the 

detection of wake vortices should be possible. Mid-wave sensors usually operate from ~3 – 5 μm, 

while long-wave sensors usually operate from ~8 – 12 μm. The 1250 – 2000 cm-1 band 

corresponds to 5 – 8 μm, which is in the opaque region; therefore, sensors do not really exist in 

this band. Also, the Suomi NPP CrIS was sent into space, and its detectors have noise levels 

representative of state of the art in current detector technology. The CrIS has a noise level of 

0.06K, or 60 mK, which is still not low enough to detect the wake vortex signal. 

11.2 Mountain Wave Turbulence 

For cost reasons, the experiments conducted under this program were ground based, not airborne, 

and that limitation is probably a key reason that the FLI program has not yet demonstrated CAT 

detection. However, the FLI program did develop a greatly improved understanding of the 

radiometric signal strength of mountain wave patterns observable from a ground-based sensor. 

FLI-FM simulated the radiance signal expected of a mountain wave pattern in order to define 

instrument requirements for the Telops Hyper-Cam at the MRS field test. The simulations were 

created at different wavenumber resolutions and at different viewing geometries (i.e. elevation 

and azimuth angles from an observation point at MRS). These simulations were done for two 

WRF models: one for a severe turbulence case from 2004, and one created for a day with 

moderate turbulence reported during the time of the field test. 

The simulations were very thorough but may have over-predicted the radiance signal due to the 

manner in which the turbulent atmosphere was modeled. A FLI-FM simulation which modeled 

the radiance signal as variability in time (rather than as a difference from a standard atmospheric 

state) indicated radiance signals on the order of 0.1 mW/m2

-cm-1-sr (see Figure 308). Because all 

simulations were done in anticipation of and for comparison with the MRS field test, we can only 

define FLI instrument requirements for a ground-based sensor operating in the long-wave infrared 

waveband. Throughout this report, we have often referred to a “turbulent signal” or “turbulent 

atmosphere” that the Hyper-Cam measures. Again, we must stress that the Hyper-Cam was never 

intended to measure the actual turbulence fluctuations, but the standing wave pattern that is 

associated with the mountain wave turbulence. The D&P is much faster, but non-imaging, and we 

had several issues with it during the data collection. 

Therefore, we believe that a broadband imaging radiometer operating in the 8 – 12 μm region 

with a noise level on the order of 0.1 mW/m2

-cm-1-sr would be required to detect the radiance 

variability due to mountain waves from a ground-based platform. However, no simulations were 

created in other wavebands, so we cannot define an optimal waveband. We do not believe it 

would be appropriate to define the requirements for a FLI sensor until further simulations can be 

performed at different wavebands and for horizontal paths at different altitudes. Furthermore, 
289 

given the opportunity, it would be beneficial to repeat the MRS measurements at flight altitudes 

(flight test), or at least for horizontal paths at a much higher elevation. 

11.3 Runway Surface Conditions 

We have had success in classifying materials in staged scenes based upon their emissivity with 

the MLEV method. However, the successful analyses were performede on staged scenes, and a 

gold plate was always used to characterize the background. It is true that for most materials the 

emission term dominates the at-surface radiance term; however, due to the spectral nature of 

incident radiance, it cannot be neglected. We still have not found a substitute for the gold plate. 

Additionally, the emissivities of ice and water are nearly identical in the waveband studied. As 

shown in Section 6.4, classification based on emissivity comparison might work better if the 

waveband were changed to 715 to 900 cm-1. As noted in Section 6.4, the Hyper-Cam range of 

800 – 1350 cm-1 is reduced when one considers that the sensor becomes noisy at either extreme of 

the spectral range (see Appendix A). However, changing the waveband under consideration to 

715 to 900 cm-1presents another problem: starting around 12 μm (i.e. wavenumbers less than 833 

cm-1), atmospheric transmission begins to drop off rapidly. 

Therefore, we are unable to define instrument requirements for runway surface characterization at 

this time. However, we have demonstrated success with the MLEV algorithm. This algorithm 

requires high spectral resolution because it minimizes the high spectral variance that would 

propagate into the calculation of emissivity spectra from atmospheric lines if an incorrect surface 

temperature were used. To calculate emissivity for surface characterization would thus require a 

hyperspectral imaging sensor. A hyperspectral imaging sensor would not be appropriate for a FLI 

sensor because it is more than that which is required for all other hazards studied and it would 

take too long to collect the data in a real-world scenario. If certain bands could be identified in 

radiance spectra, then a multi-spectral system could be used. The hyperspectral measurements 

were to be used as a study to determine if certain bands could be identified that could be used to 

define these sensor requirements. We attempted to do band comparisons in the radiance domain, 

but due to different illumination conditions among different data sets, we realized the need to 

work in the emissivity domain. We were unable to identify an appropriate temperature and 

emissivity separation algorithm that had the potential to be effectively translated from a purely 

research demonstration to a real-world, real-time FLI sensor application. 

11.4 Requirement Summary 

The following table summarizes the sensor requirements for the aviation hazards studied. 

Table 20. FLI Sensor Requirements for Hazard Detection 

Hazard Wavelength Region Resolution Sensitivity Caveat 

Wake 

Vortices 

5 – 8 μm Broadband 1 mK 5 – 8 μm is the opaque region 

of the atmosphere, and 

sensors do not exist for this 

range 

Mountain 

Wave 

LWIR window 

region (8 – 12 μm) 

16 cm-1 50 mK Other wavelength regions 

cannot be excluded because 
290 

Turbulence they were not studied during 

this program 

Runway 

Surface 

Conditions 

LWIR window 

region and beyond 

(11 – 17 μm for 

ice/water 

discrimination) 

1 – 4 cm-1 Cannot omit gold plate in 

calculation of absolute 

emissivity) 

Runway 

Obscurations 

(animals) 

LWIR or MWIR Any thermal imager will work 

(does not require radiometric 

accuracy). 

Low Visibility LWIR 2.5 cm-1 

Distance to 

Hazard 

CO2 lines in MWIR, 

edges of LWIR 

window 

1 cm-1 

Volcanic Ash 6 – 13 μm Broadband 

microbolometer 

with narrowband 

(0.5 – 1.0 μm) 

filters 

50 mK This commercialized 

instrument uses five cameras 

with narrowband filters at 

specified bands (including 

SO2) [1]. 

Icing 11 – 17 μm 4 cm-1 or less Differences in slope of the 

indices of refraction will 

indicate differences in 

ice/water. Use this knowledge 

in conjunction with 

knowledge of temperature to 

indicate supercooled liquid. 

12 Conclusions 

The state of knowledge associated with the aviation hazards of wake vortices, clear air 

turbulence, and icing on runways has been greatly advanced. A greater understanding of the 

application/needs of real-world functionality, as well as signal strength, has been achieved 

through both models and simulations, in addition to analysis of experimental data. 

Prior to the FLI program summarized here, radiometric efforts to detect aviation hazards were 

largely experimental, and they were limited by single-line-of sight broadband instruments. In 

general, the experiments were not adequately supported by simulation and modeling, and truth 

data were usually not recorded in the field, so it was not possible to know what the radiometers 

were looking at. Despite tantalizing results dating back to the late 1970s, radiometric detection of 

aviation hazards did not live up to its promise (the exception is volcanic ash detection research, 

which resulted in commercial instruments). In the FLI program, we investigated the use of 

imaging radiometers with high spectral resolution, using both modeling/simulation and field 

experiments, along with sophisticated data analysis techniques that were originally developed for 

analysis of data from space-based radiometers. By these means, we have greatly advanced the 

state of knowledge in this technical area. For cost reasons, the experiments were ground based, 

not airborne, and that limitation is probably a key reason that the FLI program has not yet 

demonstrated CAT detection. However, the FLI program did develop a greatly improved 

understanding of the radiometric signal strength of aviation hazards in a wide range of scenarios, 
291 

along with a much better understanding of the real-world functionality requirements for hazard 

detection instruments. 

12.1 FLI Forward Model Updates 

The fast computations of FLI simulated radiance with FLI-FM are based on the pre-calculated 

optical depth (OD) look-up-table. A newer version of the table was prepared in FLI-FM2 which 

implements the following improvements: 

1. An extended wavenumber range (625–2475 cm-1 instead of 650–1650 cm-1) has been 

implemented; 

2. Six atmospheric models are incorporated instead of one. 

3. LBLRTM pre-computed optical depths have been converted into transmittances for each 

layer and then convolved with a double Fourier transform (instead OD of boxcar averaged). 

4. Bottom and top Jacobians (derivatives) of the transmittance τ(ν) are included instead of 

bottom only. 

The model updates lead to better performance of the FLI-FM2 in terms of its agreement with 

LBLRTM (refer back to Figure 25 and Figure 26). 

Validation of the FLI Forward Model was not performed. However, a verification of FLI-FM2 for 

one particular case was completed through comparison of FLI-FM2 with LBLRTM. The small 

residuals, which did not exceed 0.3 K for most of the wavenumber range, calculated from the 

difference in transmittances computed with both FLI-FM2 and LBLRTM, indicate good 

agreement. The only exception was seen in the ozone band, which may be explained by the fact 

that the ozone is a fixed gas in FLI-FM2 and its spectral dependence on the input temperature is 

not taken into account. 

A comparison of LBLRTM simulated radiances and MRS measured data was completed to check 

the radiometric calibration of the observed Hyper-Cam data. These results did not match well; 

there is relative agreement in the spectral shape of the observed and simulated radiances but there 

are many regions where there is considerable disagreement between the observed and calculated 

radiance spectra. Also, there is considerable disagreement in the absolute magnitude of the 

radiances with the observed radiances being generally higher than the simulated radiances, except 

in the water vapor absorption region near 1300 cm-1. 

These systematic differences may be explained by the following two points: 

1. The use of an average Denver radiosonde to represent the atmospheric state at the time of 

the Telops observations. 

2. The use of the US-1976 standard ozone profile since no in-situ observations of ozone 

were available. 
292 

True validation of the FLI-FM2 would require dedicated experiments with sondes and ozone 

profilers. 

12.2 Wake Vortices 

Daniels [23] simulated the atmospheric temperature, moisture, and exhaust gas conditions 

associated with wake vortices from commercial jet aircraft using TASS; Daniels then used 

LBLRTM to simulate measurements of the infrared radiance spectrum that would be measured 

with a spectrometer when viewing the atmospheric disturbance created by the wake of a Boeing 

747 jet aircraft. The results indicate there is a small, but evident, temperature and moisture 

structure signature of the wake vortex disturbance.

Wake vortex radiance simulations were then conducted using FLI-FM2 to estimate the magnitude 

of and spectral location of the signal from a broadband instrument sensitive in the SW, MW, and 

LW spectral regions. Three regions were found where the wake vortex signal is a maximum; 

these are located at 700 cm-1, 1250 cm-1, and 2000–2200 cm-1. 

The measurements of the radiance associated with wake vortices generated by commercial jet 

aircraft were conducted both at Madison, Wisconsin (June, 2008) and Atlanta, Georgia (July 

through September 2010) using the Telops Mid-wave Hyper-Cam and Long-wave Hyper-Cam 

instruments, respectively. Analyses of the data collected did not reveal any detectable radiance 

signal of the wake vortices being observed. Note, however, that in Madison winds were 

extremely high and likely prevented vortices from forming, or moved them from the field of view 

before they formed, and that the Atlanta data acquisition activity was meant as preparation for a 

formal data acquisition activity to understand requirements and instrument settings. Seeing no 

observable signals in Atlanta preempted the formal acquisition activity. 

12.3 Mountain Wave Turbulence 

At 16 cm-1 resolution, the Hyper-Cam NESR is approximately 0.25 mW/m2-sr-cm-1 (see 

Appendix A), which is higher than the simulated turbulence radiance signal in time variability. 

Simulations run prior to the MRS field test indicated that we should have been able to see the 

turbulence with the Hyper-Cam. It has been noted that the high simulated signals could have been 

an artifact of the modeling. However, it could be possible that during the field test at MRS, we 

never collected any Hyper-Cam data of mountain wave disturbances that were high enough in 

severity to be observed. All simulations done prior to the test were for the March 6, 2004 severe 

turbulence WRF model. During the test, even though conditions for turbulence were predicted by 

STAR and Lenticular clouds indicative of mountain waves were present, there were no mountain 

wave turbulence PIREPs higher than “Moderate.” Mountain waves are standing waves and vary 

only slowly with time, but temperature and water vapor concentration are not homogeneous at 

any given altitude when the wave pattern is present, and it is that disturbance that leads to the 

radiance variations across a scene as shown in previous simulations. 

The plan was to separate the standing wave pattern from the turbulence on the basis of the time 

scales. The D&P TurboFT was to be implemented to obtain the fast variations caused by 

turbulence, as indicated was possible in our earlier Boulder data collection activity [34]. The 

Telops Hyper-Cam was going to provide a two-dimensional image of the scene on a longer time 
293 

scale to view the standing mountain wave pattern. During the test, we experienced trouble with 

the D&P, and the Hyper-Cam could not see evidence of the standing wave pattern. We know we 

had standing wave patterns though because of the presence of lenticular clouds and satellite 

imagery. Turbulence is typically associated with mountain waves, but is not a given. 

We knew that the analysis of ground-based measurements would be more challenging than flightbased measurements due to the reduced radiance signal for a slant path to space (see Section 

5.1.1, in particular, Figure 69 and Figure 72). Despite the smaller expected signal, we did not 

believe it would be so small that it would be unobservable. We believed the radiance signal 

would still be above the noise level of the Hyper-Cam because the FLI-FM simulations indicated 

it would. As the simulations were performed for a ground-based slant path, they inherently take 

transmission through layers of atmosphere into account. They may have over-predicted the 

signals due to the manner in which the turbulent signal was modeled. When modeling turbulence 

as a difference between a perturbed state and a standard atmosphere, a temperature offset will 

always exist, and will cause a constant bias between the two atmospheric states. For the 

simulations, the difference between perturbed radiances at different times (i.e. the standard 

atmosphere has not been subtracted) shows actual signal due to scene variability. The differences 

between perturbed and unperturbed radiances are mostly due to how the standard atmosphere was 

defined. 

We do believe that, given the opportunity, mountain wave turbulence measurements should be 

repeated at flight altitude. The FLI-FM models and simulations developed within this program 

indicate that the turbulent signal is too small to be seen with a Hyper-Cam, but these simulations 

were done at ground level in anticipation of and for comparison with MRS field test 

measurements. If the sensor were above the layers of atmosphere where attenuation will decrease 

the signal, it is possible the turbulent signal could be observed. Unfortunately, budgetary 

limitations prevented a flight test during this program. 

12.4 Runway Surface Conditions 

The ability to detect the surface emissivity for various types of runway surface conditions with a 

FLI is important for the determination of runway friction and aircraft stopping distance. However, 

surface emissivity is a difficult property to calculate because it is difficult if not impossible to 

measure all variables at measurement time. It is true that for most materials the emission term 

dominates the at-surface radiance term; however, due to the spectral nature of incident radiance, it 

cannot be neglected. The incident radiance a very difficult quantity to measure; it requires 

simultaneous measurement of a Lambertian reflector made of a low emissivity material and with 

known temperature. We used a roughened gold plate, which is standard practice, but it only exists 

in experimental set-ups; it is not practical for real-world scenarios. 

We have had success in classifying materials in staged scenes based upon their emissivity with 

the MLEV method; however, we were unable to determine the surface emissivity without the 

gold plate measurement. Also, in defining requirements for a FLI sensor, for ice vs. water, 

classification based on emissivity comparison might work better if the waveband were changed to 

715 to 900 cm-1. Original plots showed that the 800-1350 cm-1 operating region of the Telops 

Hyper-Cam was appropriate, with the differences for ice and water appearing mainly in the 700-
294 

800 cm-1 range; however the NESR of the Telops Hyper-Cam made measurements in that region 

not possible. 

The hyperspectral measurements were to be used as a study to determine if certain bands could be 

identified that could be used to define FLI sensor requirements. We attempted to do band 

comparisons in the radiance domain, but due to different illumination conditions among different 

data sets, we realized the need to work in the emissivity domain. We were unable to identify an 

appropriate temperature and emissivity separation algorithm that had the potential to be 

effectively translated from a purely research demonstration to a real-world, real-time FLI sensor 

application. 

12.5 Low Visibility 

Based on the sensitivity studies in the previous Phase 1 FLI project, an imaging FLI spectrometer 

is expected to provide enhanced vision in the cockpit of an aircraft in low visibility conditions. 

An algorithm was demonstrated under a restricted set of aircraft altitude and atmospheric 

conditions. Data of varying visibility due to fog was collected in Atlanta on several occasions. 

However, after initial preview, it was determined that data collected in thirty-minute intervals on 

the same day would be better suited for the analysis. The project team was unable to collect this 

sporadic weather condition during the remainder of the project. 

12.6 In-Flight Icing 

In-flight icing could not be studied during this program, since no flight tests were conducted. 

However, previous investigations have shown that the indices of refraction for ice and water 

differ starting at 11 μm, therefore providing the opportunity for differentiation. That knowledge 

combined with the knowledge of the temperature of the cloud to diagnose the existence of 

supercooled liquid water in clouds could be used to detect water droplets at sub-freezing 

temperatures that causes airframe icing. 

12.7 Volcanic Ash 

Detection of volcanic ash has not been investigated by the FLI team since methods of modeling 

the movement of volcanic ash (PUFF model) and instruments for its detection (Airborne Hazards 

Detection System, Norwegian Institute for Atmospheric Research) were being commercialized at 

approximately the same time as this investigation. This report summarizes the literature and 

patents related to volcanic ash detection. 

12.8 Distance to Hazard 

The ability to determine the distance to a hazard relies on the collection of high spectral 

resolution data (1 cm-1). Because we have shifted to lower spectral resolution data (16 cm-1) to 

try to detect the signal of the hazards, we have inherently lost the ability to determine the distance 

to the hazard using the CO2 line structure. However, an additional dedicated detector with two 

narrowband filters that can look at the amplitudes of the CO2 lines has been proposed as the 

method of determining the distance to the hazard should the FLI instrument be developed. 
295 

12.9 Recommendations 

For cost reasons, the experiments conducted under this program were ground based, not airborne, 

and that limitation is probably a key reason that the FLI program has not yet demonstrated CAT 

detection. However, the FLI program did develop a greatly improved understanding of the 

radiometric signal strength of aviation hazards in a wide range of scenarios. We do believe that, 

given the opportunity, mountain wave turbulence measurements should be repeated at flight 

altitude. The FLI-FM models and simulations developed within this program indicate that the 

turbulent signal is too small to be seen with a Hyper-Cam, but these simulations were done at 

ground level in anticipation of and for comparison with MRS field test measurements. If the 

sensor were above the layers of atmosphere where attenuation will decrease the signal, it is 

possible the turbulent signal could be observed. The increase in signal should be quantified by 

simulations prior to any flight testing. 

In-flight icing is another hazard that could be studied in more detail with the ability to perform 

airborne data collection activities. Ground-based differentiation of water and ice is recommended 

prior to going airborne. This program investigated that capability under runway hazards, but the 

effective range of the Telops instrument ends at about the same wavelength as the beginning of 

the divergence of the water and ice indices of refraction. Therefore, the Telops instrument was 

unable to make the distinction consistently across multiple scenes, even though an algorithm 

could be tailored for a single scene. Single line-of-sight instruments exist in this range and may 

be adequate to make this determination effectively across multiple scenes. 

Low visibility conditions could be further investigated as well. After an initial look at the data 

collected under this program, it was determined that data collected in thirty-minute intervals on 

the same day would be better suited for the analysis than what was collected. The project team 

was unable to collect this sporadic weather condition during the remainder of the project. 

However, if we had been able to, the algorithms developed by Hampton University could have 

been tested for effectiveness. Again, airborne tests would be ideal after some initial analysis of 

ground-based data. 

13 Conferences, Papers, and Presentations 

1. AvSafe 2009: GTRI personnel attended the Aviation Safety (AvSafe) Conference held in 

November 2009. 

2. AMS 2010: Hampton University and STAR Institute personnel participated in the 

American Meteorological Society (AMS) Conference held January 17-21, 2010 in 

Atlanta, GA. 

3. WakeNet 3: GTRI attended and presented at the WakeNet3 Workshop (Mar 29-30, 

2010). While at the conference, we learned that ATL has an operational WindTracer 

Lidar. It would be good to work with the FAA and Volpe to use the lidar at ATL as truth 

data for the project. Steve Lang at the FAA and Frank Wang at Volpe are in charge of the 

WindTracer Lidar installation and operation. Steve Hannon who works for the 

manufacturer of the WindTracer gave an update on current locations of operation for 

WindTracer Lidars. The WindTracer at SFO is currently not operational but should be 
296 

repaired soon, STL is still operational, HOU no longer has one; HOU’s WindTracer is the 

one that now resides in ATL. 

4. L. West, G. Gimmestad, W. Smith, L. Cornman, S. Lane, S. Kireev, and T. Daniels, 

“Airborne Forward Looking Interferometer for the Detection of Terminal-Area Hazards,” 

poster presented by G. Gimmestad at the AvSafe meeting; St. Louis, MO; May 2011. 

5. S. Lane., L. West, G. Gimmestad, W. Smith, and S. Kireev; “Surface characterization 

measurements with the Hyper-Cam,” Telops Scientific Workshop; Baltimore, MD; May 

2011. 

6. W. Smith, L. West, G. Gimmestad, and S. Lane, “Ultraspectral measurements of surface 

emissivity with an imaging interferometer spectrometer,” in Hyperspectral Imaging and 

Sounding of the Environment, 2011, OSA Technical Digest (CD) (Optical Society of 

America, 2011), paper HTuC4. (see Appendix B) 

Special Note: This paper was among the top five downloaded HISE InfoBase papers in 

2012. 

7. S. E. Lane, L. L. West, G. G. Gimmestad, W. L. Smith, Sr., and E. M. Burdette, 

“Detection of aircraft exhaust in hyperspectral image data,” in Proc. SPIE. 8158, Imaging 

Spectrometry XVI, 2011, doi: 10.1117/12.894078 (see Appendix C) 

8. S. E. Lane, L. L. West, G. G. Gimmestad, S. Kireev, W. L. Smith, E. M. Burdette, T. 

Daniels, and L. Cornman, “Hyperspectral image turbulence measurements of the 

atmosphere,” in Proc. SPIE. 8355, Infrared Imaging Systems: Design, Analysis, 

Modeling, and Testing XXIII, 2012, doi: 10.1117/12.919386 (see Appendix D) 

9. P. R. Schaffner, T. S. Daniels, L. L. West, G. G. Gimmestad, S. E. Lane, E. M. Burdette, 

W. L Smith, S. Kireev, L. Cornman, R. D. Sharman, “Experimental validation of a 

forward looking interferometer for detection of clear air turbulence due to mountain 

waves,” in 4th AIAA Atmospheric and Space Environments Conference, 2012, doi: 

10.2514/6.2012-2790 (see Appendix E) 

10. T. S. Daniels, W. L Smith, and S. Kireev, “Recent Developments on Airborne Forward 

Looking Interferometer for the Detection of Wake Vortices,” in 4th AIAA Atmospheric 

and Space Environments Conference, 2012, doi: 10.2514/6.2012-2791 

11. S. Lane., L. West, G. Gimmestad; W. Smith; and S. Kireev; “Analysis of atmospheric 

data collected over the Colorado Rockies”; Telops Scientific Workshop; Annapolis, MD; 

Sept 2012. 

12. S. Lane., L. West, G. Gimmestad; W. Smith; e. Burdette, S. Kireev; and J.M. Cathcart, 

“Analysis of atmospheric data collected over the Colorado Rockies and Sky Radiance 

Measurements at Georgia Tech”; Telops Scientific Workshop; Quebec; Sept 2013. 
297 

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Appendix A: Instrument Characterization 

Throughout this report, we have often referred to the “noise limit” or “noise floor” of the Hyper-Cam 

instrument as it relates to whether or not a simulated signal can be seen. Prior to the MRS field test, we 

had one measurement of NESR the Hyper-Cam system, which was calculated from a measurement of a 

uniform patch of sky at 1 cm-1 resolution and with 31 interferograms averaged. The plot is shown in 

Figure 361. This NESR was computed from the test described in Section 3.3.1. 

Figure 361. Telops Hyper-Cam NESR calculated from a uniform patch of sky. 

However, the NESR will change for different resolutions, integration times, and averaging. Therefore, the 

above test should not be taken as representative of the expected NESR. Typical NESR at 16 cm-1
301 

resolution for different Hyper-Cam systems can be found in the Telops Hyper-Cam Specifications Flyer, 

and is 0.20 mW/m2-sr-cm-1 at 10 μm (Long-wave Hyper-Cam), 0.04 mW/m2-sr-cm-1 at 5 μm (Mid-wave 

Hyper-Cam), and 0.06 mW/m2

-sr-cm-1 at 5 μm (Mid-wave Extended Hyper-Cam) [43]. Note that the 

Mid-wave system used in the previous phase of this project was the 3 – 5 μm Mid-wave system, not the 

Mid-wave Extended, which has a range of 1.5 – 5 μm. 

The NESR of our Hyper-Cam system was measured after the MRS test for three different resolutions, a 

250 μs integration time, and no averaging. NESR should be calculated from a measurement of a 

blackbody at a uniform temperature which fills the FOV. Because the NESR changes with spectral 

resolution, we also needed to conduct this measurement at 16 cm-1 resolution, in order to compare with 

the simulated radiances as described in Section 5.4. For completeness, the NESR was calculated for 1, 4, 

and 16 cm-1 resolutions, which have all been used as set resolutions in Hyper-Cam tests throughout the 

FLI program. 

We used measurements of the Hyper-Cam’s own blackbody (at a set temperature) as a scene in order to 

estimate the NESR. During the test, the ambient air temperature in the room was approximately 24.8 °C; 

therefore, for calibration, the two blackbodies were set at 20.0 and 30.0 °C, while for the measurement, 

the second blackbody was set to 25.0 °C. The integration time for all tests was 250 μs, which matched the 

integration time used in the MRS tests. The order of measurement, which was repeated for each 

wavenumber resolution, was: 

1. Calibration with BB1 set at 20.0 °C and BB2 set at 30.0 °C. 

2. Measurement of BB2 set at 25.0 °C. 

3. Calibration with BB1 set at 20.0 °C and BB2 set at 30.0 °C. 

Time was given between each step for the temperature set point of BB2 to stabilize. 

After calibration, each 25.0 °C blackbody radiance datacube was corrected for bad pixels. Then, the 

NESR was estimated by calculating the standard deviation of all pixels at each wavenumber. The 

resulting NESR plots for 1, 4, and 16 cm-1 resolution measurements are shown in the following three 

figures. Note the radiance units are in mW/(m2

-sr-cm-1), for consistency with those reported in the 

simulated results of Section 5.4. 
302 

Figure 362. Telops Hyper-Cam NESR at 1 cm-1 resolution. 

Figure 363. Telops Hyper-Cam NESR at 4 cm-1 resolution. 
303 

Figure 364. Telops Hyper-Cam NESR at 16 cm-1 resolution. 

Figure 365 below shows the NESR at all three resolutions on the same plot. The two higher resolution 

NESRs were downsampled with Matlab’s interp1 function to match the sampling interval of the 16 cm-1 

datacube. Neglecting the first and last three channels in the 41 channel 16 cm-1 resolution datacube (as 

well as the spike seen in the 4 cm-1 resolution data), the 4 cm-1 resolution NESR is lower than the 1 cm-1 

resolution NESR by a factor of 1.89 (approximately √4 ൌ 2), and the 16 cm-1 resolution NESR is lower 

than the 1 cm-1 resolution NESR by a factor of 3.37 (approximately √16 ൌ 4). 

Figure 365. Telops Hyper-Cam NESR at all resolutions. 
304 

The important conclusion from this test is that it is a quantitative measure of the Hyper-Cam noise floor. 

However, the values are plotted in radiance units, and in this report, we often refer to brightness 

temperature differences. Therefore, both the NESR values (average, over Hyper-Cam wavenumber range) 

and its corresponding brightness temperature difference at 300 Kelvin are listed in Table 21. 

Table 21. Hyper-Cam measured noise characteristics. 

 NESR (mW/m2-sr-cm-1) BTD @ 300 K &1000 cm-1 (milli-Kelvin)

1 cm-1 Resolution 0.85 530 

4 cm-1 Resolution 0.45 280 

16 cm-1 Resolution 0.25 160 

For completeness, we also measured the NESR of the D&P at 4 cm-1 resolution for 4 and 24 coadded 

spectra (these correspond to the settings used at MRS in 2011 and in Boulder in 2008, respectively). This 

was done in the same manner as the Hyper-Cam test except that the cold and warm blackbodies used for 

calibration were set at 15.0 °C and 35.0 °C, respectively; however, the blackbody for measurement was 

set at 25.0 °C as in the Hyper-Cam tests. The NESR plots are shown in in Figure 366. The D&P range is 

625 – 3950 cm-1; however, the wavenumber axis has been constrained to 600 – 2600 cm-1 in this figure; 

beyond that the noise is too high. Note the two “U” shapes typical of the NESR in the figure. This shape 

is due to the fact that the D&P TurboFT uses two detectors, a MCT detector is used for the LWIR (625 – 

2000 cm-1) and an InSb detector is used to extend the range to the SWIR and MWIR regions (3950 cm-1) 

[44]. The detector change-over seems to be located at 1800 cm-1 in our sensor. The D&P TurboFT NESR 

in Figure 366 has the same units as that shown for the Hyper-Cam in Figure 365; the average NESR of 

the D&P in the effective Telops Hyper-Cam range (~900-1300 cm-1) is nearly equivalent when one 

compares the Hyper-Cam 4 cm-1 resolution NESR (0.45 mW/m2

-sr-cm-1) to the D&P 4 cm-1 resolution, 4 

coadd NESR (0.41 mW/m2

-sr-cm-1). The 4 coadded data were compared because that was the setting used 

at MRS. 
305 

Figure 366. D&P TurboFT NESR. 

The average D&P NESR values measured at different wavenumber regions are shown below in Table 22. 

The 900-1300 cm-1 region is in the LWIR (7.7 – 11.1 μm), and the 1900-2600 cm-1 region is in the MWIR 

(3.9 – 5.3 μm). The LWIR region was chosen based upon the effective waveband of the Telops 

Hyper-Cam, the MWIR region was chosen somewhat arbitrarily based upon where the D&P noise level is 

low (i.e. the bottom of the “U”). 

Table 22. D&P TurboFT measured noise statistics at 4 cm-1 Resolution 

 NESR (mW/m2-sr-cm-1) BTD (milli-Kelvin) 

 900-1300 cm-1 1900-2600 cm-1 @ 300 K & 1000 cm-1 @ 448 K & 2250 cm-1

4 coadds 0.42 0.15 260 100 

24 coadds 0.16 0.06 100 40 

 
306 

Appendix B: 2011 OSA Paper 

Ultra-spectral measurements of surface emissivity with an imaging 

interferometer spectrometer 

IN OSA HYPERSPECTRAL IMAGING AND SOUNDING OF THE ENVIRONMENT 

TORONTO, CANADA 

JULY 2011 

 
307 

Ultra-spectral measurements of surface emissivity with an 

imaging interferometer spectrometer 

William Smith Sr.1, Leanne West2, Gary Gimmestad2, and Sarah Lane2 1

 Hampton University, Hampton VA 

2 Georgia Tech Research Institute, Atlanta GA 

bill.l.smith@cox.net

Abstract: Surface emissivity and skin temperature measurements were conducted with the Telops 

Hyper-Cam imaging spectrometer for a scene consisting of wet, dry, and ice covered concrete and 

a wet, dry, and ice covered non-skid surface. 

OCIS codes: 010.1615, 280.4991 

1. Introduction 

An experiment was conducted under clear sky conditions in September 2010 to simulate the ultra-spectral 

emissivity imaging of runways under different surface conditions. Emissivity images, obtained from an aircraft 

mounted Forward Looking Interferometer (FLI), are intended to be used to alert the pilot of hazardous landing 

runway surface conditions and enable the determination of runway friction and associated stopping distance for his 

particular aircraft. A parking deck on the Georgia Tech campus provided an ideal location; the fifth level was high 

enough to allow for a view of the horizon over surrounding buildings and trees. Additionally, the concrete surface 

was treated with a non-skid coating, but on only half the deck, allowing two surfaces to be imaged. The setup is 

shown in Figure 1. 

Figure 1. Parking deck setup on Georgia Tech campus, (a) Top View, (b) Side View 

The roughened gold-coated surface that is shown in Figure 1 is an InfraGold target from Labsphere with an 

emissivity of approximately 0.01 in the longwave infrared. The target allows the measurement of the diffuse 

reflection of the downwelling radiance from the sky surrounding in the same scene as the surface targets. The 

Hyper-Cam was oriented down at the scene at a 9  declination angle. After each downward-looking scene image, 

the Hyper-Cam was oriented up 9 at the sky to measure the sky radiance component. The elevation is shown in 

Figure 1 (b). A diffusivity factor determined from the ratio of the gold target radiance to the sky radiance was 

applied to enable surface emissivity and skin temperature to be estimated for every pixel within the imaging array of 

the Hyper-Cam instrument. The Field of Regard (FOR) of the Hyper-Cam consists of a 320 x 256 detector pixel 

array with each pixel having a Field of View (FOV) of 0.35 rad. The unapodized spectral resolution is 0.5 cm-1. 

The radiance spectra were calibrated using two blackbody references with temperatures of 1.0 C and 55.0 C. The 

sample integration time is 200 s. Since the scene to be imaged was static, averages of ten interferograms per 

blackbody measurement and ten interferograms per scene measurement were made to reduce the effect of detector 
308 

noise. The surface scene consisted of wet, dry, and ice covered concrete and wet, dry, and ice covered non-skid 

surfaces. 

2. Method 

Governing Equations: The radiative transfer equation governing the radiance (R) as a function of wavenumber 

() observed, with the Hyper-Cam placed a few feet above the surface, at the local zenith angle () is 

 R(,) = (,)B(,Tsfc) + (,) Rsky(,), (1) 

where the first term is the surface radiance reaching the Hyper-Cam and the second term is the surface reflected 

downwelling sky radiance reaching the Hyper-Cam. Here, (,) is the surface emissivity, B(,Tsfc) is the Planck 

radiance corresponding to the surface temperature, Tsfc, and (,) is the surface reflectivity. To arrive at equation 

(1), it is assumed that the atmospheric transmittance of the short path between the Hyper-Cam and the surface is 

unity, which is particularly true for the atmospheric window region (8-12 m) observed with the Hyper-Cam. If we 

also assume that Kirchoff’s law holds for the frequency () and measurement angle (), then (,) = [1 - (,)], 

so that 

 R(,) = (,)B(,Tsfc) + [1-(,)] Rsky(,) (2) 

and 

 (,)= [R(,) - Rsky(,)] / [B(,Tsfc) - Rsky(,)]. (3) 

The surface conditions observed were: wet, dry, and ice covered concrete and wet, dry, and ice covered nonskid surfaces. The wet covered concrete and non-skid concrete conditions were observed for different surface 

temperature and water temperature contrast conditions (i.e. warm water over cold concrete and non-skid concrete 

and cold water over warm concrete and non-skid concrete, as provided by the melting of the ice). The measurement 

sequence was (1) calibration BB views, (2) sky views, (3) surface views, (4) sky views, and (5) calibration 

blackbody views. 

Diffuse Sky Radiance Estimation: The sky view obtained with the Hyper-Cam is for an elevation angle of about 9° 

and as a result the radiance observed does not represent the angularly integrated sky radiance being reflected by the 

surface. In order to account for this angular dependence, a diffuse gold plate was placed within the FOR of the 

Hyper-Cam measurements. Since gold is a nearly perfect reflector at infrared wavelengths (i.e. reflectance > 0.99), 

surface measurements for those pixels whose FOV are filled with the gold plate provide measurements of the diffuse 

sky radiance needed for the computation of emissivity from the radiance for pixels viewing the surrounding concrete 

and non-skid concrete surfaces. In order to estimate the diffuse sky radiance for those pixels not viewing the gold 

plate, a spectral diffusivity factor, r(ν), is defined from those pixels viewing the gold plate. This diffusivity factor is 

computed from equation (1): 

r(ν) = [Rg(ν) – εg B(ν,Tg)] / [(1 – εg) Rsky(ν,θ)] (4) 

where Rg(ν) is the radiance measured from the pixels viewing the gold plate with emissivity εg and temperature Tg, 

and Rsky(ν,θ) is the observed sky radiance. An average spectral diffusivity factor is calculated for all pixels viewing 

the gold plate, and is then multiplied by Rsky(ν,θ) to obtain the diffuse sky radiance, Rdsky(ν,θ). This result is then 

used as an estimation of the diffuse sky radiance for all pixels in the Hyper-Cam array. 

 

Surface Temperature and Emissivity Determination: The surface skin temperature is defined as that value that 

minimizes the local spectral variance in the emissivity calculated using equation (3). The emissivity calculated 

according to equation (3) will exhibit excessive local spectral variance for an incorrect surface skin temperature 

since atmospheric emission line features observed in the sky radiance (see figure 1 above) will propagate into the 

calculated surface emissivity spectrum [1]. Since the local spectral variance of the true surface emissivity is small 

compared to that produced by atmospheric emission line features, the correct surface skin temperature must be that 

temperature which produces minimal local spectral variance in the derived surface emissivity spectrum. The local 

variance of the surface emissivity spectrum is calculated as the standard deviation of the spectral derivatives of 

emissivity around every spectral point. The correct surface skin temperature, and associated surface emissivity 

spectrum, is assumed to be that which has the minimum local spectral variance of the calculated surface emissivity. 
309 

The surface temperature and associated spectral emissivity is derived by considering all temperatures within the 

range of 260 K to 360 K using a five step iterative process. In the first step, an increment of 10 K is used to define the 

temperature that minimizes the standard deviation of the local spectral derivative of the calculated surface spectral 

emissivity. Once this temperature is defined, then a 2 K increment is used within a 20 K interval centered on the firstdefined surface temperature value. The iterative process is repeated a third, fourth, and final time, reducing the 

increment by a factor of 10 after each iteration, to provide a final surface temperature resolution of mK. 

3. Results

 

Figure 2 shows images of the spectral emissivity derived for three wavelengths, 10 m (1000 cm-1), 9 m (1100 

cm-1), and 8.3 m (1200 cm-1), along with the derived surface skin temperature. Significant differences are evident 

in the emissivity for the three wavelengths shown, depending on the surface condition, thereby providing a 

significant measure of the surface condition. As can be seen, ice and water have very little spectral variation, 

whereas dry concrete and non-skid surfaces provide large variations in spectral emissivity, dependent on their 

coverage with ice and/or water. Most interesting is the apparent accuracy of the surface skin temperature retrieved as 

that value which minimizes the local surface spectral emissivity variation. In-situ measurements, which were made 

with a hand-held thermistor probe, indicated the following surface temperatures: the dry concrete = 40 C (313 K), 

wet concrete = 34 C (307 K), dry non-skid pavement = 36 C (309.0 K), wet non-skid pavement = 18 C (291 K), ice 

= 0 C (273 K), and the ice water = 7 C (280K). The retrieved surface temperatures shown in figure 2 for these 

different surface conditions are in excellent agreement with these in-situ surface temperature measurements. Figure 

3 shows example spectra of the derived surface emissivity for a few representative locations within the FOR of the 

Hyper-Cam instrument. It is clearly seen that the major spectral emissivity signal of different surface conditions is 

within the 1000 - 1250 cm-1 (8 -10 m) spectral region. It is this spectral region that will be most useful for 

estimating runway surface condition and aircraft stopping distance from FLI measurements. 

 

4. Summary and Conclusions 

Measurements of surface and sky radiance were conducted with the Telops Hyper-Cam imaging interferometer. 

The scene, consisting of wet, dry, and ice covered concrete and wet, dry, and ice covered non-skid surfaces, 

possessed a large variability of emissivity conditions. By minimizing the variance of the local derivative of spectral 

emissivity, the physical temperature of the surface skin was determined simultaneously with the surface emissivity 

spectrum. This technique will be used to validate airborne and satellite emissivity spectra determinations. 

5. References 

[1] Huang, H-L, W. L. Smith, J. Li, P. Antonelli, X. Wu, R. O. Knuteson, B. Huang, B. J. Osborne, 2004: Minimum Local Emissivity Variance 

Retrieval of Cloud Altitude and Effective Spectral Emissivity—Simulation and Initial Verification. J. Appl. Meteor., 43, 795–809. 

Acknowledgements: 

The research described here was funded by the NASA Aeronautics Research Mission Directorate (ARMD) Aviation Safety (AvSafe) Program, 

under the Atmospheric Hazard Sensing & Mitigation (AHSM) area within the Atmospheric Environment Safety Technologies (AEST) project.
310 

Appendix C: 2011 SPIE Paper

Detection of Aircraft Exhaust in Hyperspectral Image Data 

IN SPIE PROCEEDINGS 8158 

IMAGING SPECTROMETRY XVI 

SAN DIEGO, CA 

AUGUST 2011 
311 

Detection of Aircraft Exhaust in Hyperspectral Image Data 

Sarah E. Lanea, Leanne L. Westa, Gary G. Gimmestada, 

William L. Smith, Sr.b

, Edward M. Burdettea 

a

Georgia Tech Research Institute, 925 Dalney Street, Atlanta, GA 30332 

b

Hampton University, 21 Tyler Street, Hampton, VA 23668 

ABSTRACT 

The use of a hyperspectral imaging system for the detection of gases has been investigated, and algorithms have 

been developed for various applications. Of particular interest here is the ability to use these algorithms in the 

detection of the wake disturbances trailing an aircraft. A dataset of long wave infrared (LWIR) hyperspectral data 

cubes taken with a Telops Hyper-Cam at Hartsfield-Jackson International Airport in Atlanta, Georgia is 

investigated. The methodology presented here assumes that the aircraft engine exhaust gases will become entrained 

in wake vortices that develop; therefore, if the exhaust can be detected upon exiting the engines, it can be followed 

through subsequent data cubes until the vortex disturbance is detected. Gases known to exist in aircraft exhaust are 

modeled, and the Adaptive Coherence/Cosine Estimator (ACE) is used to search for these gases. Although wake 

vortices have not been found in the data, an unknown disturbance following the passage of the aircraft has been 

discovered. 

Keywords: hyperspectral image processing, standoff gas detection, remote sensing, aviation safety

1. INTRODUCTION 

Aviation hazards, such as clear air turbulence, wake vortices, icing, and hazardous runway conditions, are often 

unforeseeable. Real-time detection of these hazards from an in-flight aircraft is not currently feasible; however, one 

possible method of detection is with the use of a Forward Looking Interferometer (FLI) sensor. Here, the focus is on 

the possible detection of wake disturbances that follow aircraft, which include vortices and turbulence. This paper 

investigates the measurement parameters and processing algorithms necessary for a FLI sensor to observe the 

phenomenology of these particular aviation hazards. If the phenomenology is detectable by a FLI sensor, it could 

potentially alert flight crews to hazardous conditions, allowing the pilot time to react. The detection of these 

particular hazards could also be helpful in determining runway spacing at airports from either a ground-based or an 

airborne FLI sensor. Hyperspectral image data cubes contain more information than a FLI sensor would need in this 

application. The data cubes contain both spatial and spectral information; therefore, they can be studied to determine 

the necessary parameters for a FLI system specific to the detection of aviation hazards. 

2. MEASUREMENTS 

2.1 Data collection at Hartsfield-Jackson Airport 

A Telops LWIR Hyper-Cam1

 was used to record a series of hyperspectral data cubes at Hartsfield-Jackson 

International Airport (ATL) in Atlanta, GA in July of 2010. A construction site adjacent to the airport provided an 

ideal location for the test. The Hyper-Cam was oriented East toward oncoming aircraft as they approached for 

landing on ATL’s Runway 26R; the setup is shown in Figure 1. 

 
312 

Fig. 1. The test setup just off runway 26R at Hartsfield-Jackson International Airport in Atlanta, GA. 

Figure 2 shows the Hyper-Cam set up at the test site; the Hyper-Cam was inclined at a 10° elevation angle on its 

tripod in order to image the flights. 

Fig. 2. The Hyper-Cam set up to image oncoming aircraft. 

The clear view of the horizon allowed the data acquisition to begin before an aircraft entered the field of view of the 

Hyper-Cam. This arrangement allowed continuous collection of data cubes before, during, and after each flight in 

order to capture both the sky background before, and any trailing disturbances after, the passage of the aircraft. 

2.2 Hyper-Cam settings 

The Hyper-Cam has onboard blackbodies that allow for absolute calibration of radiance data. It has an IFOV of 0.35 

mrad and a variable window size of up to 320 x 256 pixels. A reduced window size of 200 x 75 was used; therefore 

the FOV shown by the imaged area in Figure 1 is 4.0° x 1.5°. The resolution of the Hyper-Cam is variable, and sets 

of measurements were recorded at 1, 4, and 16 cm-1 resolutions. The focus here is on the 4 cm-1 resolution data 

cubes; these data cubes contain 165 spectral bands of information between 802.9 and 1349.4 cm-1. At the 200 x 75 

window size and 4 cm-1 resolution described, each data cube is collected in 873 ms, with a 137 ms delay between 

acquisitions. 

The movement of the aircraft creates a highly dynamic scene. Indeed, the scene changes so drastically in the amount 

of time needed to acquire an entire data cube that artifacts are certain to be present. However, the interest is not in 

the aircraft itself, but the wake and/or vortices that follow the aircraft, which will not change as drastically in the 

time needed to collect one data cube. A methodology is presented that will be used in the attempt to detect these 

disturbances in the hyperspectral image data. 
313 

3. METHODOLOGY 

3.1 Gas detection 

As wake vortices form and fall, there is no way to tell where in the field of view they exist. They could form below 

the captured scene or be carried away by wind and not be captured at all. With no way to determine where in the 

imaged scene the vortices are located, a search methodology is developed. The methodology for the possible 

detection of wake vortices leverages the fact that vortices often entrain aircraft engine exhaust gases such as CO2, 

CO, NOx, and H2O2

. These gases, if they exist in the data cubes, are in mixed pixels that may show spectral 

influence from all scene elements, including the gas of interest, the background, and the intervening atmosphere. 

Because these gases are in the exhaust, if they can be detected immediately upon exiting the aircraft engine, it could 

be possible to follow the gases through the next data cubes in the sequence of measurements as they either become 

entrained in a vortex or disperse into the atmosphere. 

Algorithms for gas detection in hyperspectral data have been developed and tested in various studies, which include 

testing of the algorithms on controlled gas releases3

 and modeled data4. Various approaches include Spectral Angle 

Mappers, Matched Filters, and the Adaptive Coherence/Cosine Estimator (ACE) 3,4,5. This paper uses ACE as a 

possible means of gas detection in the hyperspectral image data cubes, although multiple algorithms have been 

investigated. 

3.2 Target and background modeling 

Gas detection algorithms commonly depend on a priori knowledge of the gas spectrum under search, e.g. the target 

gas spectrum. Under a controlled gas release, gas constituents, abundances, temperatures, and concentrations are 

known. However, in the data set under investigation, these characteristics must be estimated. 

To model the target gas spectrum effectively, the background and the atmosphere must be considered. Therefore, a 

semi-empirical target gas spectrum is created from gas cell data from the online tool Spectral Calc6

, which accesses 

the HITRAN database to model multiple types of spectra, and the actual measured data. 

The gas cell radiance spectrum is downloaded from Spectral Calc after having been smoothed with a Gaussian 

function, and the resulting spectrum is interpolated to match the spectral wavenumber values and resolution needed 

for each 4 cm-1 data cube collected. This resulting spectrum is combined with the measured data in the following 

manner. First, the data cube following the passage of the aircraft is divided into horizontal strips of sky and the 

radiance of each strip is averaged. These averages are computed for each piece of sky independently, because the 

radiance of the sky is expected to change with elevation angle. This method of averaging the radiance for horizontal 

strips of sky is similar to the Directional Mean Filter7

, with the exception that we are limiting the direction of our 

background average to the horizontal. The resulting radiances give a measure of the background atmospheric 

conditions as a function of elevation angle at the time of measurement. Next, the gas cell data is added to each 

resulting radiance average; the gas cell data is added because in this case, the hot engine exhaust is expected to act 

as an emitter against the cold sky. This method gives the semi-empirically modeled target gas spectrum for each 

elevation angle. 

3.3 Search methodology 

The ACE algorithm is run on the data cube after the aircraft has passed, using the target gas spectra estimates and 

the local averages as background. The result of the ACE algorithm gives a value, or score, for every pixel, which 

indicates how closely that pixel’s spectrum matches that of the target gas. These results can be represented by a 

scaled image, which shows the spatial location in the data cube where the target gas might exist. This image of ACE 

scores can then be filtered with a median filter, similar to the procedure described by Vallières et. al.3

. The effect of 

the median filter is to eliminate high scoring pixels that have no neighbors which score similarly high, which could 

be an indication of a false alarm. 

The process of modeling the background and target gas spectra is then repeated, this time excluding portions of the 

image that show any possible detection after the first ACE run. This repetition is essential because we do not know 

the spatial location of the gas, if it exists in the data cube, and we do not want our background estimation to be 

influenced by its spectrum. 
314 

4. PRELIMINARY RESULTS 

4.1 Data cube reduction 

It was noted that some of the radiance data was quite noisy; these noisy values appeared at the extremes of the 

wavenumber scale. To determine where in the spectrum the data is least noisy, a row of pixels was selected from a 

data cube that was recorded prior to the aircraft entering the field of view, and the NESR from the radiance of these 

pixels was calculated. The assumptions here are that the sky is featureless (e.g. no gas or other contaminants are 

present) and that the pixels in this horizontal strip of sky are uniform. This NESR plot is shown in Figure 3. 

Fig. 3. The NESR calculated from the pixels from a horizontal strip of sky. 

The NESR values were rounded to the nearest one thousandth, and the wavenumber values where the NESR was 

0.001 or less were kept. These wavenumber values were from 845.9 to 1312.2 cm-1, which corresponds to bands 15 

through 151 of the original 165 wavebands. In all further analysis, the data cube under test has been reduced from 

the full 165 to the new 141 wavebands where noise is a minimum. 

4.2 Radiance imagery 

The radiance image from the data cube during the aircraft passage is shown in Figure 4. This average is calculated 

by averaging the spectral radiance values over the 141 remaining wavebands. 

 
315 

Fig. 4. The average radiance of the data cube with the aircraft passing through the FOV. 

The data cube immediately following the passage of the aircraft (approximately one second later) is shown in Figure 

5. 

Fig. 5. The average radiance of the following data cube, approximately one second later. 

The radiance images in Figures 4 and 5 show that no disturbances are immediately apparent in the radiance images 

alone. Therefore, the search methodology that exploits the spectral domain is implemented. 

4.3 Search results 

The ACE algorithm was run on the 4 cm-1 resolution data cube immediately following the passage of an aircraft. As 

the largest component of the exhaust, water vapor at 425 K was chosen as the target under search. This temperature 

was based on a derivation of a typical aircraft exhaust temperature following a method described by Mattingly8

. 

As noted earlier, the detection algorithm is run twice, once to find any highly scoring pixels, and a second time with 

those pixels excluded from the background averages. The results of the first ACE run are shown in Figure 6. 

Fig. 6. The ACE results on the data cube following the passage of the aircraft, using every column in the background 

estimation. 

The median filter was then applied to the image shown in Figure 6; this result is displayed in Figure 7. 

Radiance (W/m2sr) Radiance (W/m2sr) 
316 

Fig. 7. The ACE results on the data cube following the passage of the aircraft, using every column in the background 

estimation, with a median filter applied. 

The results of the first run of the ACE algorithm indicated that only the left 90 columns of pixels should be averaged 

for the second run of the algorithm, due to the streaks that appear in the right half of two scaled images (Figures 6 

and 7 are 200 pixels wide). Limiting the pixels chosen as the background ensures that if these pixels do contain any 

target gas, they would not contaminate the background average and target gas spectra estimations. 

The result of the second ACE run on the data cube following the passage of the aircraft which excludes these high 

scoring pixels is shown in Figure 8. 

Fig. 8. The ACE results on the data cube following the passage of the aircraft, using columns 1-90 in the background 

estimation. 

A median filter was then applied to the ACE result of Figure 8. The filtered result is shown in Figure 9. 

Fig. 9. The ACE results on the data cube following the passage of the aircraft, using columns 1-90 in the background 

estimation, with a median filter applied. 

Two streaks appear in Figures 6 through 9 that are not apparent in the radiance image of Figure 5; by comparison 

with Figure 4, one sees the streaks are located right where the aircraft engines passed through the field of view. This 

location seems to indicate the detection of exhaust leaving the engine. 
317 

For comparison, the result of the ACE run on the same data cube following the passage of the aircraft when the 

usual method of applying the ACE is applied is shown in Figure 10. The data cube spectral dimension has not been 

reduced and the sky is not divided into horizontal strips. 

Fig. 10. The ACE results on the data cube following the passage of the aircraft when the data cube is not reduced and 

the background is not divided into horizontal strips for averaging. 

The median filter was applied to Figure 10, and the result is shown in Figure 11. 

Fig. 11. The ACE results on the data cube following the passage of the aircraft when the data cube is not reduced and 

the background is not divided into horizontal strips for averaging, with a median filter applied. 

Figures 10 and 11 do not contain the streaks seen in Figures 6 through 9, although there are some pixels that do 

score more highly than others in the general area where the streaks were seen in Figures 6 through 9. 

4.3 Analysis of search results 

The score of the pixel is equal to the cosine square of the angle between the spectrum of the pixel and the spectrum 

of the target gas. Therefore, a perfect scoring pixel would have a value of one, meaning that the angle between the 

target gas spectrum and the pixel is zero. 

By comparing the actual pixel values in the images of Figures 6 through 9, the following deductions can be made. 

The first ACE run produces a maximum scoring pixel of 0.1048 in the unfiltered image of Figure 6 and a maximum 

scoring pixel of 0.0717 in the filtered image of Figure 7. Therefore, the smallest angles of separation between the 

target gas spectrum and pixel spectrum calculated from the first ACE run are 71.1136° and 74.4696° in the 

respective images. The second ACE run produces a maximum scoring pixel of 0.1270 in the unfiltered image of 

Figure 8 and a maximum scoring pixel of 0.0725 in the filtered image of Figure 9. Therefore, the smallest angles of 

separation between the target gas spectrum and pixel spectrum calculated from the second ACE run are 69.1187° 

and 74.3823° in the respective images. Figures 7 and 9 look nearly identical, and these results show that after the 

median filtering, there is only a small improvement in detection after the second ACE run. 

Most pixels in the streaks have much lower scores and therefore even higher separation angles. The results at this 

point are still inconclusive as to what is causing the disturbances seen, because these scores do not strongly indicate 

the presence of water vapor. The two distinct streaks are still interesting and their source is still being investigated. If 

the result is not a detection of the gas but perhaps an artifact of turbulence, it is still relevant to our research 
318 

regarding aviation safety. When the sky background is not divided into horizontal strips of sky for the background 

averaging and target modeling, the streaks are not observed. Therefore, this methodology does uncover some type of 

disturbance, although it is still unknown. 

The ACE algorithm was then run on following data cubes; however the distinct streaks dissipate and cannot be 

tracked through further data cubes. 

5. CONCLUSIONS 

A methodology for the detection of wake vortices in hyperspectral image data by detecting the exhaust gases that 

they entrain has been presented. However, from the results shown thus far, it is inconclusive as to whether the 

exhaust gases are actually being detected or some other disturbance is being seen. If the exhaust is indeed being 

detected, the results indicate that the detection is short lived because the same results cannot be repeated in 

successive data cubes. The method is therefore not applicable to the detection of vortices; however, the method 

presented demonstrates a way to search for mixed pixel targets when the sky is the background. Though no vortices 

were found, a disturbance was seen in the results that warrants further investigation. The low ACE scores indicate 

that gas is not being detected; however, if the disturbance is due instead to turbulence, it is still relevant in its 

applicability to aviation safety. 

ACKNOWLEDGEMENTS 

The research described here was funded by the NASA Aeronautics Research Mission Directorate (ARMD) Aviation 

Safety (AvSafe) Program, under the Atmospheric Hazard Sensing & Mitigation (AHSM) area within the 

Atmospheric Environment Safety Technologies (AEST) project. 

Access to the test location just past the runway at Hartsfield-Jackson International Airport was provided courtesy of 

Jacoby Development. 

REFERENCES 

[1] Farley V., Vallières, A., Chamberland, M., Villemaire, A., and Legault, J-F. “Performance of the FIRST, a 

longwave infrared hyperspectral imaging sensor,” Proc. SPIE 6398 (2006).

 [2] West, L. L., et al., “Hazard detection analysis for a forward-looking interferometer,” NASA Final Report 

NNX07AN17A, October 2008. 

[3] Vallières, A., Villemaire, A., Chamberland, M., Belhumeur, L., Farley, V., Griouz, J., and Legault, J-F., 

“Algorithms for Chemical Detection, Identification, and Quantification for Thermal Hyperspectral Imagers,” 

Proc. SPIE 5995 (2005). 

[4] Larrieux, E. R., “Performance evaluation of chemical plume detection and quantification algorithms” Electrical 

and Computer Engineering Master’s Theses, Dept of Electrical and Computer Engineering, Northeastern 

University. Paper 18. (2009) http://hdl.handle.net/2047/d10019297

[5] Manolakis, D. and Shaw, G., “Detection algorithms for hyperspectral imaging applications,” IEEE Signal 

Processing Magazine, 19(1), 29-43 (2002). 

[6] http://spectralcalc.com

[7] Caefer, C. E., Stefanou, M. S., Nielsen, E. D., Rizzuto, A. P., Raviv, O., and Rotman, S. R., “Analysis of false 

alarm distributions in the development and evaluation of hyperspectral point target detection algorithms,” 

Optical Engineering, 46(7), (2007). 

[8] Mattingly, J. D., Elements of Gas Turbine Propulsion, (McGraw-Hill, 1996). 
319 

Appendix D: 2012 SPIE Paper 

Hyperspectral Image Turbulence Measurements of the Atmosphere 

IN SPIE PROCEEDINGS 8355 

INFRARED IMAGING SYSTEMS: DESIGN, ANALYSIS, MODELING, AND TESTING XXIII 

BALTIMORE, MD 

MAY 2012 
320 

Hyperspectral Image Turbulence Measurements of the Atmosphere 

Sarah E. Lanea, Leanne L. Westa, Gary G. Gimmestada, 

Stanislav Kireev b, William L. Smith, Sr.b, Edward M. Burdettea, 

Taumi Danielsc

, and Larry Cornmand 

a

Georgia Tech Research Institute, Atlanta, GA 30332 

b

Hampton University, Hampton, VA 23668 c

NASA Langley Research Center, Hampton, VA 23681 

d

University Corporation for Atmospheric Research, Boulder, CO 80307 

ABSTRACT 

A Forward Looking Interferometer (FLI) sensor has the potential to be used as a means of detecting aviation hazards 

in flight. One of these hazards is mountain wave turbulence. The results from a data acquisition activity at the 

University of Colorado’s Mountain Research Station will be presented here. Hyperspectral data cubes from a Telops 

Hyper-Cam are being studied to determine if evidence of a turbulent event can be identified in the data. These data 

are then being compared with D&P TurboFT data, which are collected at a much higher time resolution and broader 

spectrum. 

Keywords: hyperspectral image processing, remote sensing, aviation safety, atmospheric turbulence

1. INTRODUCTION 

The real-time detection of atmospheric turbulence is of great interest due to the applicability to aviation safety. A 

FLI sensor has the potential to be used as a means of detecting aviation hazards in flight, giving the flight crew time 

to react. One of these hazards is mountain wave turbulence. Mountain waves are often associated with the presence 

of lenticular clouds, which pilots will avoid, and normally persist all day, so that pilot reports (PIREPS) can be used 

to avoid areas of moderate to severe turbulence. However, there have been reports1

 of unexpected encounters with 

mountain waves and turbulence that have resulted in damage to aircraft, injury, and loss of life that make the 

capability of real-time detection of these events desirable. 

The results from a data acquisition activity at the Mountain Research Station near Boulder, Colorado during 

November of 2011 are presented here. This site experiences mountain waves and mountain wave turbulence in the 

fall and winter months, which affect flights into Denver International Airport. The two main objectives of this test 

were as follows: 

 Observe slowly varying radiance patterns associated with standing waves. 

 Observe short-term fluctuations associated with turbulence. 

These objectives require that long data sets (approximately thirty minutes) at as high as possible temporal resolution 

be collected of the sky by interferometric means. This collection activity utilized both a long wave infrared Telops 

Hyper-Cam and a Designs and Prototypes (D&P) TurboFT spectrometer, both operating in continuous mode 

simultaneously for approximately thirty minutes for each data collection event. Each sensor provides unique 

information about the sky; the Hyper-Cam collects LWIR hyperspectral image data of the scene, while the TurboFT 

has the capability to collect approximately 100 spectra per second in the 2.5-16 micron region. Both sensors have 

on-board blackbodies, which allow for calibrated radiance in the field. The two sensors were co-located on a custom 

built mount which allowed boresighting of the sensors for the test so that they would be observing the same area of 

sky throughout each measurement period. 

The Hyper-Cam data addressed the first objective in that the long data sets could capture slowly varying radiance, 

while the TurboFT data addressed the second objective in that the high temporal resolution and broader spectrum 

could capture short-term fluctuations. Both datasets are being investigated as a means of studying the feasibility of 

using a FLI sensor for the detection of turbulent events; this paper presents preliminary results from these data. 
321 

2. DATA COLLECTION ACTIVITY 

2.1 Mountain Research Station Test Site 

The Mountain Research Station (MRS) is located on the front range of the Rocky Mountains at an elevation of 2,900 

m (9,500 ft), approximately 76 km (47 mi) WNW of Denver International Airport. This elevation and clear line of 

sight towards the airport makes the Mountain Research Station an ideal location for observing mountain waves that 

form over the Rockies. 

Several sites at elevations higher than the main lab were selected for possible locations for the data collection; 

however, due to snow cover and an in-operational Sno-Cat, the data collection was confined to the dining hall and 

parking lot at the Mountain Research Station. However, this did not inhibit the data collection; even though the 

instruments were below the tree line, looking out of an open window from the second floor of the dining hall 

provided a clear view of the sky over the trees in the direction of Denver (shown in Figure 1). 

Figure 1. Instrument set-up looking out the open window of the MRS dining hall (left), and instruments looking ESE 

toward Denver (right). 

2.2 Hyper-Cam Settings 

The Telops Hyper-Cam is a long-wave infrared hyperspectral imaging sensor2

 with variable resolution from 0.25 – 

150 cm-1 and a variable image size of up to 320 x 256 pixels. The data it provides is referred to as a data cube; the 

three dimensions of data are (x, y, σ), where x and y are spatial information (i.e. the pixel in an image), and σ is the 

spectral information (i.e. radiance at wavenumber σ). Therefore, each data cube is composed of a radiance image at 

narrow wavebands within the LW portion of the spectrum. 

The entire 320 x 256 window was used, and for the majority of the test, the Hyper-Cam was operated with a 0.25X 

magnification lens, which increased the field of view from 6.4° horizontal x 5.1° vertical FOV to 25.6° horizontal x 

20.4° vertical FOV. However, for the runs discussed in this paper, the smaller field of view was used. In these tests 

the spectral resolution was set at 16 cm-1. Sixteen wavenumber data cubes contain 41 frames in the spectral 

dimension between 810.06 and 1341.24 cm-1; each is collected in approximately 1696 ms with a 65 ms delay 

between each cube. 

This wavenumber resolution was chosen because previous models showed collection at 16 cm-1 resolution provides 

the highest temporal resolution while still capturing enough spectral information to distinguish a turbulent wave 

event3

. The recording frequency of the Hyper-Cam instrument is not high enough to capture a turbulent event; 

however the spatial information it provides over 30 minutes could make it possible to visually detect the location of 

standing waves. This information can then be used to isolate certain time periods of TurboFT spectra for analysis. 
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2.3 TurboFT Settings 

A Designs and Prototypes (D&P) TurboFT spectrometer was used simultaneously with the Hyper-Cam to provide 

spectral data at higher temporal resolution. The D&P TurboFT is a dual InSb/MCT detector instrument capable of 

providing up to 100 spectra per second from 2.5 to 16 μm. For this data collection activity, spectra were collected at 

4 cm-1 resolution and set at four coadds, which corresponds to four interferograms being averaged to produce each 

spectrum. These settings resulted in a recording rate of 12 spectra per second. This higher temporal resolution than 

that of the Hyper-Cam allows for a better chance for observation of short-term fluctuations associated with 

turbulence. 

The TurboFT sensor was equipped with a four inch telescope which provided a 1.2 degree FOV. It was determined 

early in test planning that the TurboFT 1.2 degree FOV must be aligned with the center of the Hyper-Cam FOV. A 

common mount that allowed for boresighting of the sensors, as well as protected the TurboFT from the elements, 

was developed and is explained in the next section. 

2.4 Sensor Mount 

The Hyper-Cam is capable of operation in the lower temperatures expected in Colorado in November, however, the 

safe operation of the TurboFT required that an enclosure be built for it that would provide a stable thermal 

environment. An insulated aluminum box was constructed to house the TurboFT, and a heater was placed inside. A 

mount for both the Hyper-Cam and the TurboFT enclosure was then constructed from unistrut that would allow both 

the Hyper-Cam and TurboFT enclosure to be mounted via dovetail slides. The mount was constructed such that both 

sensors could be rotated from 90 to zero zenith angle viewing geometry while maintaining boresight (it should be 

noted that the mount was never rotated to the zero zenith angle position with the D&P installed to avoid damage to 

the sensor). A SolidWorks representation of the mount with both Hyper-Cam and D&P installed is shown in Figure 

2. 

Figure 2. A SolidWorks representation of the boresight mount with TurboFT in aluminum enclosure (top) and HyperCam (bottom). 

Alignment of the center of the TurboFT FOV with the center pixel of the Hyper-Cam image was tested after a 

complete tear-down and reassembly of the sensor mount both before and during the field test. 
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2.5 Ancillary Data 

During this test, the only ground truth data that exists to identify a turbulent event is from pilot reports (PIREPS) and 

Eddy Dissipation Rate (EDR) reports. During this test, PIREPS were continuously monitored by team members in 

the event that any severe turbulence was reported in the area of MRS; instruments could then be directed toward the 

location of the PIREP. During the time of the test, only moderate turbulence was reported. Full PIREP and EDR 

reports were compiled after the test. Meteorological data, including temperature, humidity, pressure, wind speed, 

and solar radiation data, was provided by MRS personnel for the dates of the test from a weather station at MRS’s 

C1 site, located 0.78 km (0.49 mi) from the test site at 3,022 m (9,912 ft) elevation. 

The Hyper-Cam comes equipped with a visible camera; however, a webcam was used instead to record visible 

imagery of the sky. While the webcam has a much wider field of view than either the Hyper-Cam or the TurboFT, it 

provided visible documentation of any obscurations to a clear field of view, such as clouds, aircraft, or aircraft 

contrails. The webcam was placed inside the TurboFT enclosure so that it too would be boresighted with all other 

instruments. 

3. METHODOLOGY AND PRELIMINARY RESULTS 

Each thirty minute data collection event produced approximately 1,000 Hyper-Cam data cubes and 20,000 TurboFT 

spectra. The Hyper-Cam data were investigated first in order to find visual cues to locations of possible waves or 

turbulence. Results from the fourth dataset from November 17, 2011 will be shown here. For this dataset, a large 

lenticular cloud was in the scene and the instruments were lined up to view the area right under the cloud, at 112° 

azimuth and 4° elevation viewing angle. Figure 3 shows the viewing geometry as recorded by the webcam. 

Figure 3. A visible image of the viewing geometry for the fourth dataset collected on Nov. 17, 2011. The red box 

shows the approximate Hyper-Cam field of view. 

3.1 Data cube Reduction 

The series of data cubes produced a four dimensional dataset (x, y, σ, t). x, y, and σ comprise the dimensions of the 

original data cube; t is the time element. Variability in the time series of data cubes was investigated first, and was 

tested only at one spectral location in the data cube at a time. A reduced data cube was created with spatial and time 

dimensions only (x, y, t) for spectral locations of interest. The following criteria were considered when choosing 

spectral locations from the original Hyper-Cam data for processing in the reduced data cubes: 
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 Choose locations in the spectral domain where any variability can be considered true variability and not 

due to noise (i.e. not at the extremes of the spectral range of the Hyper-Cam, where the NESR is higher and 

noise is more prominent in the spectrum than signal). 

 Choose locations which correspond to water vapor lines, because previous models3

 have demonstrated that 

temperature and moisture variations at these lines could be exploited for detection of waves and turbulence. 

The reduced (x, y, t) data cubes were then corrected for bad pixels in order to avoid bad values creating 

unrealistically high variability. Bad pixel correction was performed by applying a spatial filter to each image, 

whereby each pixel was compared to the average of the neighboring eight pixels, and if the absolute value of the 

difference was greater than one standard deviation of the neighbors, the pixel was replaced with the average. Due to 

the small neighborhood of surrounding pixels, there were still some areas of each image that showed high spatial 

variability, and so a median filter was applied to smooth the images. 

3.2 Data Cube Differencing by Subtraction of the Mean

After the filtering was applied to each image in the reduced data cube, the mean radiance value at each pixel index 

for all times was calculated, creating a mean image, which was then subtracted from each image in the reduced data 

cube to create a “difference cube”. Each image from the difference cube was then strung together to create a movie 

file showing the time evolution of variability. See below for the video (linked in online version) for a run from 

November 17, 2011, showing the time evolution of variability in radiance at 956.13cm-1. This wavenumber was 

chosen because models showed it exhibited the highest variability3. 

Video 1. “VariabilityMovie1.wmv”, which shows the time evolution of radiance variability in the sky scene 

under the lenticular cloud. (available at spie.org) 

3.3 Cloud Removal 

The video file shows that there are areas of high variability in the area under the lenticular cloud; however, 

comparison with the visible imagery from the webcam indicated that this variability could be due to higher radiance 

of incoming clouds at the end of the time series. By exploiting the spectral nature of the Hyper-Cam data cubes, a 

simple test was developed to classify clear sky or clouds. 

The sky is the coldest object in these scenes, more so than clouds, treetops, or any other obscuration. A comparison 

of sky radiance vs. cloud radiance is shown in Figure 4. 
325 

Figure 4. The radiance from a sky pixel compared to the radiance from a cloud pixel. 

The spectra shown in Figure 4 are from the same row of pixels so that the elevation angle for both would be the 

same and not create a difference in magnitude. These spectra are at 16 cm-1 resolution, so the narrow spectral lines 

characteristic of a sky spectrum cannot be seen in the measured sky radiance; however, there is enough difference in 

the two spectra so that a determination of sky or “other” can be made for every pixel in the scene. The cloud 

spectrum exhibits much higher radiance than the sky spectrum; additionally it has fewer spectral features and 

follows more closely the trend of a blackbody. 

In order to determine the areas of clear sky, the following procedure was followed. Frame 8 (903.02 cm-1) from each 

data cube in time was chosen to be the frame that exhibited a substantial difference between sky and cloud spectra. 

Frame 35 (1261.67 cm-1) from each data cube in time was chosen to be the frame that exhibited the most similarity 

between sky and cloud spectra. For every time in the series, frame 35 was subtracted from frame 8. A binary image 

was created for every time step, whereby if the result of the subtraction was positive, a one was assigned to the 

pixel, and if the result of the subtraction was negative, a zero was assigned to the pixel. By overlaying these binary 

images over the difference images described in the previous section, everything but clear sky should be masked, 

which aids in the determination of whether variability can be attributed to temporal changes in cloud position. 

Variability images from the beginning and end of the time series, both unmasked and masked, are shown in Figure 

5. 
326 

Figure 5. The variance in radiance at the beginning of the thirty minute run (top two images) vs. the variance in 

radiance at the end of the thirty minute run (bottom two images). The images on the right have been masked by the 

cloud removal test. 

Figure 5 demonstrates that this simple algorithm to test for sky also masks clear sky at the horizon as the atmosphere 

here is more opaque and its radiance looks more like a blackbody. Therefore, a second test was developed that 

would compare the relative change in radiance between frame 8 (903.02 cm-1) and frame 9 (916.30 cm-1). As shown 

in Figure 6, both sky spectra, regardless of elevation angle, exhibit a much greater change between the two 

wavenumbers than the cloud spectrum. 

Figure 6. The radiance from sky pixels at two elevations compared to the radiance of a cloud pixel. 
327 

The relative difference of the local radiance between 903.02 cm-1 and 916.30 cm-1 of the three spectra in Figure 6is 

8.133 x 10-4 W/m2

sr in the case of the sky at the high elevation, 2.410 x 10-4 W/m2sr in the case of the cloud, and 

7.681 x 10-4 W/m2

sr in the case of the sky at the horizon. Because the sky radiance changes approximately three 

times faster in the case of both sky spectra, this derivative is checked in addition to the radiance difference between 

903.02 cm-1 and 1261.67 cm-1. Variability images from the beginning and end of the time series, both unmasked and 

masked with the new constraint are shown in Figure 7. These images are from the same time as those shown in 

Figure 5. 

Figure 7. The variance in radiance at the beginning of the thirty minute run (top two images) vs. the variance in 

radiance at the end of the thirty minute run (bottom two images). The images on the right have been masked by the 

cloud removal test as in Figure 5, but with the additional constraint of checking the change in radiance between 903.02 

cm-1 and 916.30 cm-1. 

3.4 Data Cube Differencing Using Rank Order Statistics

Applying the cloud mask showed that the appearance of clouds in the scene later in the time series was significantly 

contributing to the variability as the higher radiance of the clouds was affecting the mean. Therefore, instead of 

calculating the mean radiance of each pixel for the entire time series, the radiances of each pixel were ranked in 

ascending order prior to calculating a mean. Every pixel in the time series was sorted by ascending radiance; the 

higher radiances that can be attributed to clouds are therefore all located in the higher quantiles and can be excluded 

from analysis. 

The upper and lower bounds for which to calculate an average radiance were chosen somewhat arbitrarily. The 

median values were found for every pixel and then these median values were subtracted from each image in the 

reduced data cube (x, y, t). Additionally, because the high radiance values are unwanted, an average was calculated 

from the ranked data between zero and 50%. This average was then subtracted from each image in the reduced data 

cube. These results were then compiled into a movie with the cloud mask applied, which can be seen in the 

following video. 
328 

Video 2. “VariabilityMovie2.wmv”, which shows the time evolution of radiance variability calculated from an average 

that includes only the first 50% of ranked radiance data. (available at spie.org)

The mean that is calculated to create the variability seen in Video 2 should not have as much cloud influence; 

however, the clouds affecting the mean still cannot be excluded. One should note the light blue band that appears in 

the clear portion of the sky between the trees. This variability change is most likely not due to clouds. The spectra of 

these pixels are currently under investigation to see if the source of variability can be identified. 

4. FUTURE WORK 

Analysis up to this point has only focused on visual examination of time evolution of variability in Hyper-Cam data. 

Because turbulence is associated with temperature and moisture variations, it could be possible to exploit the 

spectral domain of the data cubes by comparing measured radiance to water vapor spectra, modeled at different 

temperatures and concentrations, using standard gas detection algorithms. The authors have investigated these 

algorithms previously4

 as they apply to the detection of aircraft exhaust. 

In addition to investigating gas detection algorithms for hyperspectral data, the authors plan to begin analysis of the 

TurboFT data. This high-temporal-resolution spectral data will be analyzed to see if short-term fluctuations in 

radiance can be identified in the time series. These short-term fluctuations could potentially indicate turbulent 

events. If fluctuations in the TurboFT data are identified, these times will then be compared to the Hyper-Cam 

hyperspectral imagery. Similarly, if standing waves are identified in the Hyper-Cam data, these times will then be 

compared to corresponding time sequences in D&P data. 

5. CONCLUSIONS 

This paper discussed preliminary results from a data collection at Mountain Research Station near Boulder, 

Colorado in November of 2011. As of this writing, only the Hyper-Cam data cubes have been investigated. The 

visual inspection of the time evolution of variability has been used as the main metric for determining where in the 

field of view standing waves or turbulence is most likely. Further analysis is needed for a conclusive determination 

on whether or not the turbulent events can be identified in these data; however, results thus far indicate that this 

identification could be possible. 

ACKNOWLEDGEMENTS 

The research described here was funded by the NASA Aeronautics Research Mission Directorate (ARMD) Aviation 

Safety (AvSafe) Program, under the Atmospheric Hazard Sensing & Mitigation (AHSM) area within the 
329 

Atmospheric Environment Safety Technologies (AEST) project. 

The authors wish to thank Jack Wood of GTRI for designing and constructing the D&P enclosure and boresight 

mount, Kelly Matheson and Jennifer Morse of Mountain Research Station for providing access to and testing 

assistance at MRS, and Julie Prestopnik of UCAR for compiling EDR and PIREP reports.

REFERENCES 

[1] West, L., Gimmestad, G., Smith, W., Kireev, S., Cornman, L. B., Schaffner, P. R., Tsoucalas, G., “Applications 

of a Forward-Looking Interferometer for the on-board detection of aviation weather hazards,” NASA/TP-2008-

215536; Added to NTRS: 2008-12-10; Document ID: 20080045836; Report Number: L-19522. 

[2] Farley V., Vallières, A., Chamberland, M., Villemaire, A., and Legault, J-F. “Performance of FIRST, a 

longwave infrared hyperspectral imaging sensor,” Proc. SPIE 6398 (2006). 

[3] West, L., Gimmestad, G., Smith, W., Kireev, S., Daniels, T., Cornman, L., “Airborne Forward Looking 

Interferometer for the Detection of Terminal-Area Hazards,” NASA Contract NNX09AR67A Annual Report, 

Year 2 (July 2011). 

[4] Lane, S. E., West, L. L., Gimmestad, G. G., Smith, W. L., and Burdette, E. M., “Detection of aircraft exhaust in 

hyperspectral image data,” Proc. SPIE 8158 (2011). 

 
330 

Appendix E: 2012 AIAA Paper 

Experimental Validation of a Forward Looking Interferometer for 

Detection of Clear Air Turbulence due to Mountain Waves 

IN THE 4TH AIAA ATMOSPHERIC AND SPACE ENVIRONMENTS CONFERENCE 

NEW ORLEANS, LA 

JUNE 2012 

 
331 

Experimental Validation of a Forward Looking 

Interferometer for Detection of Clear Air Turbulence due to 

Mountain Waves 

Philip R. Schaffner1 and Taumi S. Daniels2

NASA Langley Research Center, Hampton, Virginia 23681 

Leanne L. West3

, Gary G. Gimmestad4 Sarah E. Lane5, and Edward M. Burdette6

Georgia Tech Research Institute, Atlanta, Georgia 30332 

William L. Smith7

 and Stanislav Kireev8

Hampton University, Hampton, Virginia 23668 

Larry Cornman 9

 and Robert D. Sharman 10

Science and Technology in Atmospheric Research [STAR] Institute, Boulder, Colorado 80301 

The Forward-Looking Interferometer (FLI) is an airborne sensor concept for detection 

and estimation of potential atmospheric hazards to aircraft. To be commercially viable such 

a sensor should address multiple hazards to justify the costs of development, certification, 

installation, training, and maintenance. The FLI concept is based on high-resolution 

infrared Fourier Transform Spectrometry (FTS) technologies that have been developed for 

satellite remote sensing. These technologies have also been applied to the detection of 

aerosols and gases for other purposes. The FLI is being evaluated for its potential to address 

multiple hazards, during all phases of flight, including clear air turbulence (CAT), volcanic 

ash, wake vortices, low slant range visibility, dry wind shear, and icing. In addition, the FLI 

is being evaluated for its potential to detect hazardous runway conditions during landing, 

such as wet or icy asphalt or concrete. The validation of model-based instrument and hazard 

simulation results is accomplished by comparing predicted performance against empirical 

data. Models for FLI measurables for mountain wave turbulence were developed during the 

previous phases of the project. Prior to the field campaign, these models were used to predict 

what the sensors should have been able to detect, based on expected instrument 

performance. After the data collection activities, the empirical data was used to update and 

validate the existing models. This iterative process was employed during the course of the 

project as new empirical results became available. Previous research programs, focused on 

forward-looking airborne sensors such as Doppler radars and lidars to detect and forecast 

turbulence, have produced many tools for analysis, modeling, and simulation. Following on 

the methods used in the airborne radar turbulence detection problem, relationships between 

the statistics of an atmospheric disturbance (such as the temperature field) and those of the 

sensor measurements (the spectral radiance) will be developed. In the mountain lee wave 

 

1

 Aerospace Technologist, Research Directorate Electromagnetics & Sensors Branch, 8 N Dryden Street, MS 473. 2

 Aerospace Technologist, Research Directorate Electromagnetics & Sensors Branch, 8 N Dryden Street, MS 473. 3

 Principal Research Scientist and Director, Landmarc Research Center, Electro-Optical Systems Laboratory, 925 

Dalney St., MS 0834. 

4

 Principal Research Scientist, Electro-Optical Systems Laboratory, 925 Dalney St., MS 0834, AIAA Member. 5

 Research Scientist I, Electro-Optical Systems Laboratory, 925 Dalney St., MS 0841. 

6

 Research Scientist I, Electro-Optical Systems Laboratory, 925 Dalney St., MS 0841. 

7

 Distinguished Professor, Dept. of Atmospheric and Planetary Sciences, 21 Tyler Street. 

8

 Research Assistant Professor, Dept. of Atmospheric and Planetary Sciences, 21 Tyler Street. 9

 Project Scientist, Research Applications Laboratory, Aviation Applications Program, Senior Member AIAA . 10 Project Scientist, Research Applications Laboratory. 
332 

data collected in the previous FLI project, the data showed a damped, periodic mountain 

wave structure. The wave data itself will be of use in forecast and nowcast turbulence 

products such as the Graphical Turbulence Guidance (GTG) and Graphical Turbulence 

Guidance Nowcast (GTG-N) products. Determining how turbulence hazard estimates can be 

derived from FLI measurements will require further investigation. 

I. Introduction

HE Forward-Looking Interferometer (FLI) is an airborne sensor concept for detection and estimation of 

potential atmospheric hazards to aircraft. To be commercially viable such a sensor should address multiple 

hazards to justify the costs of development, certification, installation, training, and maintenance. The FLI concept is 

based on high-resolution infrared Fourier Transform Spectrometry (FTS) technologies that have been developed for 

satellite remote sensing. These technologies have also been applied to the detection of aerosols and gases for other 

purposes. The FLI is being evaluated for its potential to address multiple hazards, during all phases of flight, 

including clear air turbulence (CAT), volcanic ash, wake vortices, low slant range visibility, dry wind shear, and 

icing. In addition, the FLI is being evaluated for its potential to detect hazardous runway conditions during landing, 

such as wet or icy asphalt or concrete. 

Prior research has addressed the physical basis for radiometric detection of these hazards.1-3 The ability of the 

FLI to provide estimates of the range to in-flight hazards was also investigated, with the result that both detection 

and ranging are enabled by the high spectral resolution provided by the FTS. A sufficient infrared spectral signature 

appeared to be associated with the hazards to enable detection and/or mitigation of all of them, although detection of 

CAT at relevant ranges may also require that the FLI have high temperature resolution, dependent on a good signalto-noise ratio. The combination of high spectral and temperature resolutions in an imaging instrument is expected to 

enable sophisticated algorithms with high detection rates and low false alarm rates. The FLI will also function as an 

infrared imager, providing a real-time video display with night vision capability and enhanced vision in obscured 

conditions. These capabilities will enable the FLI to detect runway obstructions during landing, including both 

vehicles and wild game or livestock. 

The current project includes several ground-based field data collection events for hazards, including runway 

surface state and contamination, low visibility conditions, wake vortices, and mountain wave turbulence. The 

research is currently focused on mountain wave turbulence, and field data collection was conducted in November 

2011 at the University of Colorado-Boulder's Mountain Research Station (MRS) near Boulder, CO. The Mountain 

Research Station (MRS) is located on the front range of the Rocky Mountains at an elevation of 2,900 meters (9,500 

feet), approximately 76 kilometers (47 miles) west northwest of Denver International Airport (DEN). This elevation 

and clear line of sight to the atmosphere above the airport make MRS an ideal location for observing mountain 

waves that form over the Rocky Mountains. The instruments were located looking out of an open window from the 

second floor of the MRS dining hall, which provided a clear view of the sky over the trees in the direction of 

Denver, as shown in Figure 1. 

T 
333 

Figure 1. MRS dining hall and instrumentation 

The mountain wave research experiment has three major elements: 1) ground-based field measurements; 2) the 

validation of models and simulations by comparing predicted results to empirical data; and 3) the development of 

improved sensor models and hazard metrics. Simulations to determine test requirements Weather Research and 

Forecast (WRF) model data - temperature and H2O profiles) were generated from an actual lee wave event on March 

6, 2004. Although these fields do not develop identically for all cases, the model is perhaps representative of what 

one could expect of mountain waves in general. The vertical cross-section of the water vapor perturbations for this 

representative case is shown in Figure 2. 

Figure 2. Vertical cross-section of water vapor for lee wave turbulent event, 

March 6, 2004. 
334 

In these simulations, the observer was placed at the Hyper-Cam location at MRS and directed east at 15, 30, 45, 

60, and 90° elevation angles. The brightness temperature differences from the azimuthal mean from the simulation 

that most closely match the actual data collection configuration at MRS are shown in Figure 3; note that although 

the color scale was limited to +/- 0.5 K in order to accentuate signals beyond these limits, the results from the 

simulations in general did indicate that signals of 1 Kelvin or more should be observable. Distance to turbulence 

should be predictable from the spectrum due to differences in absorption along the sides of the spectral window, 

though this capability is only possible if the data is collected at 1cm-1 resolution. The data collected at MRS in 

November was collected at 1 cm-1, 4 cm-1 and 16 cm-1 resolutions. The 1 cm-1 datasets have not yet been 

investigated. 

Figure 3. Brightness temperature difference (Kelvin) from the azimuthal 

mean.

II. Experimental Instrumentation

Instrumentation for these field tests included both a Telops Long Wave InfraRed (LWIR) Hyper-Cam 

interferometric imager and a Designs and Prototypes (D&P) TurboFT single pixel spectro-radiometer. Simulations 

have shown that mountain waves will cause a pattern of brightness temperature differences that should be 

observable with both the Hyper-Cam and the D&P. The Hyper-Cam combines imaging with high spectral resolution 

in the 870 – 1300 cm-1 infrared window region, but it requires times of seconds to minutes to acquire a single data 

cube, depending on the resolution and number of pixels used. These acquisition times are appropriate for mountain 

waves, which are almost stationary. These measurements were taken at the MRS, located at an altitude of 9,500 feet, 

so that measurements were gathered in a forward-looking manner, per the diagram in Figure 4 below. 

(Azimuth ) deg

-8 -6 -4 -2 0 2 4 6 8

(Elevation angle ) deg

-6

-4

-2

0

2

4

6

-0.5 

-0.4 

-0.3 

-0.2 

-0.1 

0.0 

0.1 

0.2 

0.3 

0.4 

0.5 

Viewing Elev. Angle for Central Pixel  o,  cm-1
335 

Figure 4. Mountain Wave Data Collection 

The D&P spectrometer is complementary to the Telops instrument: it is very fast, with a set resolution of 4 cm-1

and a wide spectral range of 625 – 5000 cm-1, with a single line of sight. In a previous phase of the program, data 

was collected at the NCAR Foothills Lab in Boulder, CO, in 2008. That data consists of sets of 5,000 spectra 

recorded at three per second, and statistical analyses of those time series have revealed fluctuations in the 

atmospheric spectral absorption window regions that are different on windy days and calm days. The D&P is 

expected to enable detection of fast fluctuations associated with turbulent air motion. In addition, its extended 

spectral range enables further analysis in the mid-wave infrared (MWIR) region of the spectrum. 

III. Ancillary Data Collection

Other data collected includes weather and satellite data. Information such as winds, temperature, humidity, 

visibility, etc. will be valuable for later modeling and is important to understanding the sensor data collected. 

Atmospheric data was provided by the MRS on-site weather station and the Science and Technology in Atmospheric 

Research (STAR) Institute located about 45 minutes away in Boulder, CO. The location of the tests, on the front 

range of the Rocky Mountains, was advantageous for acquiring truth data due to the amount of air traffic in the area 

primarily comprising flights into, out of, and at cruise altitudes over the Denver International Airport (DEN). Air 

traffic provided a source for pilot reports (PIREPS) and automated reports from United Airlines (UAL) 757 and 

Delta Airlines (DAL) 737 aircraft as “ground truth” for the turbulence detection efforts. 

The UAL and DAL aircraft run algorithms to estimate and automatically report eddy dissipation rates (EDR) to 

the one-third power. These reports consist of the peak and mean value of the EDR over the previous minute (in 

cruise). These reports cover “none” to “extreme” levels of turbulence. The truth data allow for the evaluation of both 

true detections and false alarms by the sensor and algorithms. Pilot reports (PIREPS) are subjective in nature. 

PIREPS are not always given for turbulence encounters (even severe ones); they are rarely given for null turbulence; 

the reported time and position can be in error; and the intensity level of the turbulence reported by the pilot is a 

subjective measure of the actual intensity level. Nevertheless, these data, when available, are valuable in the 

verification process. Figure 5 shows the flight tracks of the EDR-equipped UAL 757s over a 24-hour period. It can 

be seen that there are many flights in the Colorado area. 
336 

Figure 5. UAL 757 reports over a 24-hour period. Colors are assigned based on severity of turbulence. 

(0 = None/Smooth; 0.1 = Smooth to Light; 0.2 = Light; 0.3 = Light to Moderate; 0.4 = Moderate; 0.5 = 

Moderate to Severe; 0.6 = Severe; 0.7 = Severe to Extreme) 

A useful tool for monitoring turbulence activity during the field test was the “EDR viewer.” This viewer allows 

for the display of real-time and archival EDR data sets, which are displayed as color-coded square dots. 

Furthermore, the EDR viewer contains turbulence forecast information from the Graphical Turbulence Guidance 

(GTG) forecast product. The GTG data was used in real time during a data collection period and in archival mode 

for the verification effort. The viewer can also generate vertical cross-sections. 

Additionally, MODIS satellite imagery was viewed during the test to identify days with mountain waves. A 

MODIS image from November 17, 2011 is shown in Figure 6. This figure clearly shows evidence of mountain 

waves along the Rocky Mountains. 

Figure 6. MODIS Imagery over Colorado Rockies, November 17, 

2011. Band 27 (6.535 – 6.895 micron band) 

 
337 

IV. Data Analysis

Due to technical difficulties, some data sets collected at MRS in 2011 lacked not only webcam imagery to verify 

or exclude the existence of clouds, but also D&P data. However, several runs from November 17, 2011 had 

complete data sets and lenticular clouds were present. Lenticular clouds are formed in a standing wave pattern if 

there is sufficient moisture in the atmosphere and are an indication of mountain wave activity. During the 4th time 

series from November 17, clouds were crossing the instrument field of view (FOV), which resulted in significantly 

larger disturbances of the measured signal than was observed in other data analyzed. This difference in disturbance 

is clearly seen in Figure 7, which shows spectral dependence for temporal variability of the signal in radiance units 

for all 8 runs processed to date. Figure 8 shows the same quantity in brightness temperature units for Nov. 17, run 4 

only. The difference in disturbance is estimated by a calculation of temporal variability. This variability is estimated 

by performing statistics over all data cubes for a given run after subtracting the row mean radiance for each 

cube/channel separately to eliminate the radiance dependence on the viewing elevation angle. 

Figure 7. Spectral dependence of the temporal 

variability of the scene obtained by averaging over all 

image pixels for a given channel, 8 runs total, 

radiance units. 

Figure 8. Spectral dependence of the temporal 

variability of the scene obtained by averaging over all 

image pixels for given channel, , brightness temperature 

units. 

wavenumber, cm-1

900 1000 1100 1200 1300

Temporal Variability, Rad units 0.0005

0.0010

0.0015

0.0020

0.0025

Nov16, run1

Nov16, run2

Nov16, run3

Nov16, run4

Nov16, run6

Nov17, run7

Nov17, run8

Nov17, run4

wavenumber, cm-1

900 1000 1100 1200 1300

Temporal Variability, BT units [K]

0.5

1.0

1.5

2.0

2.5

3.0
338 

The computation of temporal variability produces one cube of data, which contains the estimated 

standard deviation for each pixel/channel separately. Averaging the standard deviation over all pixels, 

separately for each spectral channel, provides spectral dependence of the computed temporal variability. 

This result is presented in Figure 9 and Figure 10. Corresponding images of temporal variability for 

channels 4 and 23 are shown in Figure 9 and Figure 10. The most visible patterns in Figure 9 and Figure 10 

are caused by cloud motion across the FOV during time series of measurements (see Figure 11). 

Figure 9. Image of temporal variability for channel 4, radiance units (left panel) and brightness 

temperature units (right panel): Nov 17, run 4. 

Figure 10. Image of temporal variability for channel 23, radiance units (left panel) and brightness 

temperature units (right panel): Nov 17, run4. 

80 64 48 32 16

16

32

48

64

0.0 

0.5 

1.0 

1.5 

2.0 

2.5 

3.0 

3.5 

4.0 

80 64 48 32 16

16

32

48

64

0.0000 

0.0005 

0.0010 

0.0015 

0.0020 

0.0025 

0.0030 

0.0035 

0.0040 

Temporal Variability

Nov 17,run 4, Channel 4 (~850 cm‐1)

Rad. Scale 00.04 W/m2/cm‐1/sr

Temporal Variability

Nov 17,run 4, Channel 4 (~850 cm‐1)

BrT. Scale 04 K

80 64 48 32 16

16

32

48

64

0.0 

0.5 

1.0 

1.5 

2.0 

2.5 

3.0 

3.5 

4.0 

80 64 48 32 16

16

32

48

64

0.0000 

0.0005 

0.0010 

0.0015 

0.0020 

0.0025 

0.0030 

0.0035 

0.0040 

Temporal Variability

Nov 17,run 4, Channel 23 (~1102 cm‐1

)

Rad. Scale 00.04 W/m2

/cm‐1/sr

Temporal Variability

Nov 17,run 4, Channel 23 (~1102 cm‐1

)

BrT. Scale 04 K
339 

Figure 11. From left to right: channel #5 from the first, middle, and last cubes of data, Nov 17, run 

4. One can see how clouds are moving during time of the observation. 

In the above results, temporal variability had been computed over all good pixels, including cloudy 

ones. Now, we exclude them from the statistical averaging in order to reveal more clearly the temporal 

variability without cloud contamination. The cloud brightness temperature was estimated to be about 240 

K. We have performed computations for cloud brightness temperatures (TCLD ) from 215 K to 250 K with 5 

K increments to see how this threshold value affects the temporal variability pictures. A cloud mask was 

applied in such a way that all pixels with radiance (R) values higher than R (TCLD=215, 250, 5 K) have been 

excluded from the consideration. This exclusion leads to a different number of pixels averaged over 1007 

data cubes total: for example, as can be seen in Figure 11, the upper and bottom thirds of the whole frame 

are always excluded, while for the central 1/3 area, the number of averaged pixels is changing from 1007 

(always clear) to 0 (always cloudy) from left to right. The number of averaged pixels depends also on the 

given value of threshold cloud temperature. 

The temporal variability results, with cloud contributions over a threshold value (TCLD=240 K) being 

eliminated, are presented in Figure 12. Eight channels with central wavenumbers from 863 cm-1 to 1115 

cm-1 are shown in brightness temperature units, with the same scale of 0 - 4 K for each panel. 

The temporal variability when TCLD varies from 215 K to 250 K is presented in Figure 13 for the 

transparent channel at ~903 cm-1. One can note that even as low as 215 K, there is cloud signal. For 

comparison, Figure 14 shows temporal variability for the opaque channel at ~1035 cm-1 (O3 band). The 

panels shown are for threshold values of TCLD = 260 K, 265 K, and 270 K. There is no signal for TCLD ≤

255 K in this channel. Note also that the color scale is a factor of 2 smaller (0 - 2 K) in Figure 15 than in 

Figure 12 and Figure 13. 
340 

Figure 12. The temporal variability of the scene when cloudy pixels TB > 240 K are eliminated. 

Unit = brightness temperature, the same color scale 0-4 K is used for all panels; Nov 17, run 4. 
341 

Figure 13. The temporal variability of the scene for the transparent channel #8 (~903 cm-1) when 

different cloud brightness temperature threshold values are applied (215 K to 250K, increment 5 K); 

Nov 17, run 4. 

Figure 14. The temporal variability of the scene for the opaque channel #18 (~1035 cm-1) when 

different cloud brightness temperature threshold values are applied (260, 265,270 K); Nov 17, run 4. 

Note that color scale is a factor of two smaller (0-2 K) than the previous figures. There are no pixels 

for TCLD ≤ 255 K. 

This study indicates that much of the variability seen is likely due to cloud variations, though more 

analysis is necessary. In a previous paper, we used different analysis techniques on the same data set.4

 In 

this analysis, the sky at the horizon was not excluded from analysis. The cloud mask applied by Lane in [4] 

was based on spectral features of cloud versus sky, as opposed to temperature. A figure from this paper, 

which shows the high sky radiance at lower elevation, is shown in Figure 15. Even though the sky radiance 

is higher, there are still spectral features which differentiate it from clouds. 
342 

Figure 15. The radiance spectra from sky pixels at two 

elevations compared to the radiance of a cloud pixel.4

However, further analysis has shown that this portion of the sky is covered by a variable haze. 

Comparison of a truly clear day with the data from November 17th shows the difference in the horizon. 

Figure 16 shows two LWIR images from the data collection activity at MRS. The left image is from 

November 16th, a completely clear day, while the image on the right is from November 17th. Note that the 

FOV’s of the two images are not the same. For comparison, the green box in the left image outlines the 

approximate FOV of the right image. There is still a haze evident at the horizon. This haze is natural and 

expected, even on the clearest of days; the radiance will be higher toward the horizon and will decrease 

with increasing elevation angle. On the 17th, however, we believe that there is a thicker haze than is 

expected when compared with numbers from the clear day. The brightness temperatures for the clear day, 

shown at 903 cm-1 (the same channel as displayed for a cloudy day in Figure 13), are shown in Figure 17. 

On this clear day, approximate brightness temperature in the haze seems to indicate that 215K could be a 

valid threshold for cloud masking. Further investigation and understanding is still warranted. 

 

Figure 16. LWIR images from November 16, a clear day versus November 17, a cloudy day. 

Images shown are at 929.6 cm-1. The radiance is scaled in W/m2

sr. 
343 

Figure 17. The brightness temperature of the sky on November 16, 

a clear day. Brightness temperature in Kelvin. 

V. Conclusion

This paper presents a follow-on analysis to the SPIE paper4 referenced regarding the atmospheric data 

collected over the Colorado Rockies, near Boulder, CO. Brightness temperature differences were used in 

the identification of clouds, as opposed to spectral features of clouds versus sky. When the clouds are 

removed using the cloud brightness temperature threshold values, the variability is almost eliminated. In 

addition, further spectral analysis from the SPIE paper of clear sky versus haze along and above the 

horizon, an area that was masked by the cloud brightness temperature threshold used here, showed that the 

variability is likely due to hazy clouds that are not apparent in the visible spectrum. 

Although these studies have not shown the expected results as seen in simulations, some of the expected 

results could be masked by the noise threshold of the data collection instrumentation. It may still be 

possible to see mountain waves with the Hyper-Cam; there is much remaining data to be analyzed. 

However, we are in the process of identifying an instrument with at least an order of magnitude reduction 

in measurement noise, relative to the Hyper-Cam. The Hyper-Cam sensitive range is 870 to 1300 

wavenumbers. Research has shown sensitive regions for detection of atmospheric temperature variations in 

the lower troposphere are specific narrow bands: 670-750, 1250-1450, 1580-1660, 1800-2100, and 2200-

2350 wavenumbers.5

 This could lead to the design of new instruments optimized for detection of 

atmospheric turbulence. Lastly, even if the Telops Hyper-Cam is unable to detect the expected signal of a 

mountain wave, we expect that the turbulence associated with mountain waves may still be evident in the 

data from the D&P spectro-radiometer. 

Acknowledgments

The NASA Aeronautics Research Mission Directorate (ARMD) Aviation Safety (AvSafe) Program, under 

the Atmospheric Hazard Sensing & Mitigation (AHSM) area within the Atmospheric Environment Safety 

Technologies (AEST) project, funded the research described here. In addition, the authors would like to 

thank personnel from the Mountain Research Station for their guidance and assistance with the data 

collection activity presented here. 

References

1

Gimmestad, G. G., Papanicolopoulos, C. D., Richards, M. A., Sherman, D. L., and West, L. L.; "Feasibility Study of 

Radiometry for Airborne Detection of Aviation Hazards." Funded by the NASA Aviation Safety Program Intelligent 

Integrated Flight Deck Project, (NASA/CR-2001-210855 under contract NAS1-99073), June 2001. 2

West, L .L., Gimmestad, G.G., Smith, W .L., Kireev, S., Cornman, L. B., Schaffner, P. R.; Tsoucalas, G.: Applications 

of a Forward-Looking Interferometer (FLI) for the On-board Detection of Aviation Weather Hazards. NASA Technical 

Publication TP-2008-215536, Oct. 2008 
344 

3

West, L. L., Gimmestad, G.G., Smith, W.L., Kireev, S., Daniels, T., Cornman, L., Sharman, R., Weekley, A., Perram, 

G., Gross, K., Smith, G., Feltz, W., Taylor, J., and Olson, E., “Hazard Detection Analysis for a Forward-Looking 

Interferometer.” Final Report NASA/ NNX07AN17A, Oct 31, 2008. 4

Lane, S. E., West, L. L., Gimmestad, G.G., Kireev, S., Smith, W.L., Burdette, E. M., Daniels T. S., and Cornman. L., 

“Hyperspectral Image Turbulence Measurements of the Atmosphere,” Proceedings of SPIE Infrared Imaging Systems: 

Design, Analysis, Modeling, and Testing XXIII, Vol. 8355, Baltimore, MD, 2012. 5

Daniels, T. S., Smith, W. L., and Kirev, S.: 2012, “Recent Developments on Airborne Forward Looking Interferometer 

for the Detection of Wake Vortices”, AIAA-1302175, 4th AIAA Atmospheric and Space Environments Conference, 

New Orleans, LA
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